#HOME = "C:\\Users\\paslanpatir\\Documents\\GitHub\\Soostone\\"
HOME = "C:\\Users\\pelin.yurdadon\\Desktop\\Soostone\\"
setwd(HOME)
options(scipen=999)
options(warnings = -1)
library(data.table)
library(tidyverse)
library(ggplot2)
library(gridExtra)
library(corrplot)
library(treemapify)
library(lubridate)
library(stringr); library(stringi)
library(skimr)
library(zoo)
library(Hmisc)
library(mice) ## imputation
library(VIM)
library(e1071) # svm
library(glmnet) # lasso
library(xgboost) #xgboost
#library(caret) # for general ml applications
library(cluster) # clustering algorithms
library(factoextra) # clustering algorithms & visualization
-- Attaching packages ------------------------------------------------------------------------------- tidyverse 1.3.1 -- v ggplot2 3.3.5 v purrr 0.3.4 v tibble 3.1.1 v dplyr 1.0.6 v tidyr 1.1.3 v stringr 1.4.0 v readr 1.4.0 v forcats 0.5.1 -- Conflicts ---------------------------------------------------------------------------------- tidyverse_conflicts() -- x dplyr::between() masks data.table::between() x dplyr::filter() masks stats::filter() x dplyr::first() masks data.table::first() x purrr::flatten() masks jsonlite::flatten() x dplyr::lag() masks stats::lag() x dplyr::last() masks data.table::last() x purrr::transpose() masks data.table::transpose() Attaching package: 'gridExtra' The following object is masked from 'package:dplyr': combine corrplot 0.91 loaded Attaching package: 'lubridate' The following objects are masked from 'package:data.table': hour, isoweek, mday, minute, month, quarter, second, wday, week, yday, year The following objects are masked from 'package:base': date, intersect, setdiff, union Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: lattice Loading required package: survival Loading required package: Formula Attaching package: 'Hmisc' The following objects are masked from 'package:dplyr': src, summarize The following objects are masked from 'package:base': format.pval, units Attaching package: 'mice' The following object is masked from 'package:stats': filter The following objects are masked from 'package:base': cbind, rbind Loading required package: colorspace Loading required package: grid VIM is ready to use. Since version 4.0.0 the GUI is in its own package VIMGUI. Please use the package to use the new (and old) GUI. Suggestions and bug-reports can be submitted at: https://github.com/alexkowa/VIM/issues Attaching package: 'VIM' The following object is masked from 'package:datasets': sleep Attaching package: 'e1071' The following object is masked from 'package:Hmisc': impute Loading required package: Matrix Attaching package: 'Matrix' The following objects are masked from 'package:tidyr': expand, pack, unpack Loaded glmnet 4.1-1 Attaching package: 'xgboost' The following object is masked from 'package:dplyr': slice Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
# define a function to make plain the characters
to.plain <- function(s) {
s= str_trim(str_to_lower(s, locale = "en"), side = c("both"))
s = gsub("\\s","",s)
chars = "[\\/\\(\\)\\-\\+\\&\\*]"
s= str_replace_all(s, chars, "")
return(s)
}
# change from "-" to NA
make_null = function(s,selected_char){
return(ifelse(s == "" | str_detect(s,selected_char),NA,s))
}
make_null_0 = function(s){
return(ifelse(s == 0,NA,s))
}
fill_missing = function(x,method = ""){
if(method == "mean"){
y = mean(x,na.rm = TRUE)
x[is.na(x)] = y
return(x)
}else if(method == "freq"){ # freq
y= x[!is.na(x)]
y = names(which.max(table(y)))
x[is.na(x)] = y
return(x)
}else {
return(x)
}
}
## encode some columns
encode_numeric <- function(x, order = unique(x)) {
x <- as.numeric(factor(x, levels = order, exclude = NULL))
return(x)
}
encode_grouping = function(x,Q = 10,P = NA,name_for_other = "other"){
x_dt = data.table(t(t(table(x))))
if(is.na(P)){
x_dt = x_dt[order(-N)][1:(Q-1)]
survivors = x_dt$x
}else{
sm = summary(x_dt$N)
survivors = x_dt[N > sm[[P + 1]]]$x
}
x[!x %in% survivors] = name_for_other
return(x)
}
get_scale_params = function(data, feature_columns){
x = data[,.SD,.SDcols = feature_columns]
x_scaled = scale(x,center= TRUE,scale = TRUE)
x = as.matrix(x_scaled)
Scale_Parameters = data.table("features" = attr(x_scaled,"dimnames")[[2]],
"means" = attr(x_scaled,"scaled:center"),
"sd" = attr(x_scaled,"scaled:scale"))
return(Scale_Parameters)
}
scale_external = function(dt,ScaleParams){
for(i in ScaleParams$features){
mean = ScaleParams[features == i]$means
sd = ScaleParams[features == i]$sd
sd = ifelse(sd == 0,0.000001,sd)
dt[, temp:= .SD, .SDcols = i]
dt[, temp:= (temp - mean)/ sd]
dt[,(i) := temp]
dt[, temp := NULL]
}
return(dt)
}
calc_rmse= function(x,y){ return(sqrt(mean((x-y)^2)))}
normalize_between_01 = function(x){
max = max(x)
min = min(x)
x = (x - min)/(max-min)
return(x)
}
## boxplots of different features wrt a specific column
draw_boxplot = function(dt,id_col = NULL, selected_cols){
dt_molten = dt[,.SD,.SDcols = c(id_col,selected_cols)] %>% melt.data.table(id.vars = id_col)
ggplot(data = dt_molten,aes_string(x = quo_name(id_col),fill = quo_name(id_col)))+
geom_boxplot(aes(y = value)) +
facet_wrap(~ variable, scales = "free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
}
draw_barplot = function(dt,selected_cols,group_number,bin_number = NA,ncol){
options(scipen=999)
num_cols = dt[,.SD,.SDcols = selected_cols] %>% purrr::keep(is.numeric) %>% colnames()
num_data = dt[,.SD,.SDcols = num_cols]
if(is.na(bin_number)){
num_data[, (num_cols):= lapply(.SD, Hmisc::cut2, g = group_number),.SDcols = num_cols ]
}else{
num_data[, (num_cols):= lapply(.SD, cut, breaks = bin_number,dig.lab=10),.SDcols = num_cols ]
}
new_data = cbind(dt[,.SD,.SDcols = c(setdiff(selected_cols,num_cols))],num_data)
new_data_molten = new_data %>% tidyr::gather()
ggplot(data = new_data_molten,aes(x = value, fill = key))+
facet_wrap(~ key, scales = "free",ncol = ncol) +
geom_bar() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
}
draw_corplot = function(data){
data %>% cor() %>%
corrplot::corrplot.mixed(upper = "ellipse",
lower = "number",
tl.pos = "lt",
number.cex = .5,
lower.col = "black",
tl.cex = 0.7)
}
tend_outliers_del = function(dt,focus_col, sigma = 3){
temp = dt[[focus_col]]
m = mean(temp)
sd = sd(temp)
lb = m - sigma*sd
ub = m + sigma*sd
temp = temp[temp < ub]
temp = temp[temp > lb]
m = mean(temp)
sd = sd(temp)
lb = m - sigma*sd
ub = m + sigma*sd
dt = dt[dt[[focus_col]]<ub]
dt = dt[dt[[focus_col]]>lb]
return(dt)
}
tend_outliers_keep = function(x, sigma = 3){
if(is.numeric(x) || is.integer(x)){
temp = copy(x)
m = mean(temp)
sd = sd(temp)
lb = m - sigma*sd
ub = m + sigma*sd
temp = temp[temp < ub]
temp = temp[temp > lb]
m = mean(temp)
sd = sd(temp)
lb = m - sigma*sd
ub = m + sigma*sd
temp = copy(x)
temp[temp > ub] = ub
temp[temp < lb] = lb
return(temp)
}
return(x)
}
tend_outliers_keep_IQR = function(x, IQR = 2){
if(is.numeric(x) || is.integer(x)){
temp = copy(x)
q = quantile(temp)
third = q[[4]]
max_limit= third*IQR
temp[temp> max_limit] = max_limit
return(temp)
}
return(x)
}
model_xgboost= function(feature_list,target,chunk_no = 10){
dt_model = copy(dt[,.SD,.SDcols = c("idx",target,feature_list)])
chunk_no = 10
set.seed(0)
folds <- cut(seq(1,nrow(dt_model)),breaks=chunk_no,labels=FALSE)
pred_table = data.table()
imp_table = data.table()
fitted_table = data.table()
for(i in 1:chunk_no){
#Segment your data by fold using the which() function
testIndexes <- which(folds==i,arr.ind=TRUE)
testData <- dt_model[testIndexes, ]
trainData <- dt_model[-testIndexes, ]
y_train = trainData[[target]]
y_test = testData[[target]]
Scale_Parameters = get_scale_params(trainData, feature_list)
x_train = scale(trainData[,.SD,.SDcols = feature_list])
x_test = testData[,.SD,.SDcols = feature_list]
scale_external(x_test,Scale_Parameters)
d_train= xgb.DMatrix(data = as.matrix(x_train), label = y_train)
d_test = xgb.DMatrix(data = as.matrix(x_test), label = y_test)
set.seed(i)
xg.model = xgb.train( data = d_train,
nrounds = 20,
early_stopping_rounds = 3,
params = params,
watchlist = list(train = d_train, test = d_test))
if(str_detect(target,"log") == TRUE){
xg.pred = exp(predict(xg.model, d_test))
actual = exp(y_test)
xg.fitted = exp(predict(xg.model, d_train))
}else{
xg.pred = predict(xg.model, d_test)
actual = y_test
xg.fitted = exp(predict(xg.model, d_train))
}
imp = data.table(xgb.importance( feature_names = colnames(x_train), model = xg.model))
imp_table = rbind(imp_table,data.table(chunk = i, imp))
#Check performance
sub_pred_table = testData[,.(idx, actual = actual, pred = xg.pred, chunk = i)]
sub_fitted_table = trainData[,.(idx, fitted = xg.fitted, chunk = i )]
pred_table = rbind(pred_table,sub_pred_table )
fitted_table = rbind(fitted_table,sub_fitted_table )
}
imp_table_2= dcast(imp_table, Feature ~ chunk, value.var = "Gain")
imp_table_2 = imp_table_2[order(-`5`)]
return(list(pred_table,imp_table_2))
}
model_xgboost_partial= function(feature_list,target,chunk_no = 5){
borough_list = unique(dt$borough)
b_class_list = unique(dt$b_class_group)
pred_table = data.table()
imp_table = data.table()
fitted_table = data.table()
for(b in borough_list){
for(c in b_class_list){
dt_model_sub = copy(dt[ borough == b & b_class_group == c
,.SD
,.SDcols = c("idx","borough","b_class_group",target,feature_list)])
chunk_no = 5
set.seed(0)
folds <- cut(seq(1,nrow(dt_model_sub)),breaks=chunk_no,labels=FALSE)
# some columns might include NA values for some group of data
incomplete_cols = colnames(dt_model_sub)[colSums(is.na(dt_model_sub)) !=0]
feature_list= setdiff(feature_list, incomplete_cols)
dt_model_sub = dt_model_sub[,(incomplete_cols):= NULL]
rm(incomplete_cols)
for(i in 1:chunk_no){
testIndexes <- which(folds==i,arr.ind=TRUE)
testData <- dt_model_sub[testIndexes, ]
trainData <- dt_model_sub[-testIndexes, ]
y_train = trainData[[target]]
y_test = testData[[target]]
Scale_Parameters = get_scale_params(trainData, feature_list)
x_train = scale(trainData[,.SD,.SDcols = feature_list])
x_test = testData[,.SD,.SDcols = feature_list]
scale_external(x_test,Scale_Parameters)
d_train= xgb.DMatrix(data = as.matrix(x_train), label = y_train)
d_test = xgb.DMatrix(data = as.matrix(x_test), label = y_test)
set.seed(i)
xg.model = xgb.train( data = d_train,
nrounds = 20,
early_stopping_rounds = 3,
params = params,
watchlist = list(train = d_train, test = d_test))
if(str_detect(target,"log") == TRUE){
xg.pred = exp(predict(xg.model, d_test))
actual = exp(y_test)
xg.fitted = exp(predict(xg.model, d_train))
}else{
xg.pred = predict(xg.model, d_test)
actual = y_test
xg.fitted = exp(predict(xg.model, d_train))
}
imp = data.table(xgb.importance( feature_names = colnames(x_train), model = xg.model))
imp_table = rbind(imp_table,data.table(borough = b, b_class_group = c,chunk = i, imp))
#Check performance
sub_pred_table = testData[,.(idx, actual = actual, pred = xg.pred, chunk = i,borough = b, b_class_group = c)]
sub_fitted_table = trainData[,.(idx, fitted = xg.fitted, chunk = i ,borough = b, b_class_group = c)]
pred_table = rbind(pred_table,sub_pred_table )
fitted_table = rbind(fitted_table,sub_fitted_table )
}
}
}
imp_table_2 = imp_table[, .(avg_gain = mean(Gain)),.(borough,b_class_group,Feature)]
return(list(pred_table,imp_table_2))
}
model_xgboost_partial_wo = function(feature_list,train_target,test_target,chunk_no = 5){
borough_list = unique(dt$borough)
b_class_list = unique(dt$b_class_group)
pred_table = data.table()
imp_table = data.table()
fitted_table = data.table()
for(b in borough_list){
for(c in b_class_list){
feature_list_used = copy(feature_list)
dt_model_sub = copy(dt[ borough == b & b_class_group == c
,.SD
,.SDcols = c("idx","borough","b_class_group",train_target,test_target,feature_list_used)])
chunk_no = 5
set.seed(0)
folds <- cut(seq(1,nrow(dt_model_sub)),breaks=chunk_no,labels=FALSE)
incomplete_cols = colnames(dt_model_sub)[colSums(is.na(dt_model_sub)) !=0]
feature_list_used= setdiff(feature_list_used, incomplete_cols)
dt_model_sub = dt_model_sub[,(incomplete_cols):= NULL]
rm(incomplete_cols)
for(i in 1:chunk_no){
testIndexes <- which(folds==i,arr.ind=TRUE)
testData <- dt_model_sub[testIndexes, ]
trainData <- dt_model_sub[-testIndexes, ]
y_train = trainData[[train_target]]
y_test = testData[[test_target]]
Scale_Parameters = get_scale_params(trainData, feature_list_used)
x_train = scale(trainData[,.SD,.SDcols = feature_list_used])
x_test = testData[,.SD,.SDcols = feature_list_used]
scale_external(x_test,Scale_Parameters)
d_train= xgb.DMatrix(data = as.matrix(x_train), label = y_train)
d_test = xgb.DMatrix(data = as.matrix(x_test), label = y_test)
set.seed(i)
xg.model = xgb.train( data = d_train,
nrounds = 20,
early_stopping_rounds = 3,
params = params,
watchlist = list(train = d_train, test = d_test))
if(str_detect(train_target,"log") == TRUE){
xg.pred = exp(predict(xg.model, d_test))
actual = exp(y_test)
xg.fitted = exp(predict(xg.model, d_train))
}else{
xg.pred = predict(xg.model, d_test)
actual = y_test
xg.fitted = exp(predict(xg.model, d_train))
}
imp = data.table(xgb.importance( feature_names = colnames(x_train), model = xg.model))
imp_table = rbind(imp_table,data.table(borough = b, b_class_group = c,chunk = i, imp))
#Check performance
sub_pred_table = testData[,.(idx, actual = actual, pred = xg.pred, chunk = i,borough = b, b_class_group = c)]
sub_fitted_table = trainData[,.(idx, fitted = xg.fitted, chunk = i ,borough = b, b_class_group = c)]
pred_table = rbind(pred_table,sub_pred_table )
fitted_table = rbind(fitted_table,sub_fitted_table )
}
}
}
imp_table_2 = imp_table[, .(avg_gain = mean(Gain)),.(borough,b_class_group,Feature)]
return(list(pred_table,imp_table_2))
}
Perform exploratory analysis on this dataset and produce a showcase/storyline of a few interesting patterns and your observations. You will walk us through your findings during our interview.
dt = fread("nyc-rolling-sales.csv")
colnames(dt)
t(head(dt))
| V1 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|
| BOROUGH | 1 | 1 | 1 | 1 | 1 | 1 |
| NEIGHBORHOOD | ALPHABET CITY | ALPHABET CITY | ALPHABET CITY | ALPHABET CITY | ALPHABET CITY | ALPHABET CITY |
| BUILDING CLASS CATEGORY | 07 RENTALS - WALKUP APARTMENTS | 07 RENTALS - WALKUP APARTMENTS | 07 RENTALS - WALKUP APARTMENTS | 07 RENTALS - WALKUP APARTMENTS | 07 RENTALS - WALKUP APARTMENTS | 07 RENTALS - WALKUP APARTMENTS |
| TAX CLASS AT PRESENT | 2A | 2 | 2 | 2B | 2A | 2 |
| BLOCK | 392 | 399 | 399 | 402 | 404 | 405 |
| LOT | 6 | 26 | 39 | 21 | 55 | 16 |
| EASE-MENT | NA | NA | NA | NA | NA | NA |
| BUILDING CLASS AT PRESENT | C2 | C7 | C7 | C4 | C2 | C4 |
| ADDRESS | 153 AVENUE B | 234 EAST 4TH STREET | 197 EAST 3RD STREET | 154 EAST 7TH STREET | 301 EAST 10TH STREET | 516 EAST 12TH STREET |
| APARTMENT NUMBER | ||||||
| ZIP CODE | 10009 | 10009 | 10009 | 10009 | 10009 | 10009 |
| RESIDENTIAL UNITS | 5 | 28 | 16 | 10 | 6 | 20 |
| COMMERCIAL UNITS | 0 | 3 | 1 | 0 | 0 | 0 |
| TOTAL UNITS | 5 | 31 | 17 | 10 | 6 | 20 |
| LAND SQUARE FEET | 1633 | 4616 | 2212 | 2272 | 2369 | 2581 |
| GROSS SQUARE FEET | 6440 | 18690 | 7803 | 6794 | 4615 | 9730 |
| YEAR BUILT | 1900 | 1900 | 1900 | 1913 | 1900 | 1900 |
| TAX CLASS AT TIME OF SALE | 2 | 2 | 2 | 2 | 2 | 2 |
| BUILDING CLASS AT TIME OF SALE | C2 | C7 | C7 | C4 | C2 | C4 |
| SALE PRICE | 6625000 | - | - | 3936272 | 8000000 | - |
| SALE DATE | 2017-07-19 | 2016-12-14 | 2016-12-09 | 2016-09-23 | 2016-11-17 | 2017-07-20 |
So, columns are like:
Location colunmns : Borought, neighborhood, block, lot, address, apartment number, zipcode
Building columns:
Then_and_Now columns:
Sale Price and Sale Date
#skim(dt)
Dates covered: between 2016-09-01 and 2017-08-31 (12 months)
#dt %>% keep(is.character) %>% lapply(unique)
Unique Key
# https://www.kaggle.com/new-york-city/nyc-property-sales --> 'Datasets' says that:
# The combination of borough, block, and lot forms a unique key for property in New York City. Commonly called a BBL
dt[,bbl := paste(BOROUGH,BLOCK,LOT,sep="-")]
dt[,.N,.(bbl)][ N > 1][order(-N)]%>% head()
t(dt[bbl == '1-373-40'])
t(dt[bbl == '1-1006-1302'])
| bbl | N |
|---|---|
| <chr> | <int> |
| 4-8489-1 | 166 |
| 1-94-1 | 79 |
| 1-1009-37 | 69 |
| 4-2086-50 | 56 |
| 4-6698-40 | 53 |
| 1-1006-1302 | 46 |
| V1 | 16 | 17 | 18 | 19 | 20 |
|---|---|---|---|---|---|
| BOROUGH | 1 | 1 | 1 | 1 | 1 |
| NEIGHBORHOOD | ALPHABET CITY | ALPHABET CITY | ALPHABET CITY | ALPHABET CITY | ALPHABET CITY |
| BUILDING CLASS CATEGORY | 09 COOPS - WALKUP APARTMENTS | 09 COOPS - WALKUP APARTMENTS | 09 COOPS - WALKUP APARTMENTS | 09 COOPS - WALKUP APARTMENTS | 09 COOPS - WALKUP APARTMENTS |
| TAX CLASS AT PRESENT | 2 | 2 | 2 | 2 | 2 |
| BLOCK | 373 | 373 | 373 | 373 | 373 |
| LOT | 40 | 40 | 40 | 40 | 40 |
| EASE-MENT | NA | NA | NA | NA | NA |
| BUILDING CLASS AT PRESENT | C6 | C6 | C6 | C6 | C6 |
| ADDRESS | 327 EAST 3 STREET, 1C | 327 EAST 3 STREET, 1C | 327 EAST 3 STREET, 3A | 327 EAST 3RD STREET, 5A | 327 EAST 3 STREET, 2E |
| APARTMENT NUMBER | |||||
| ZIP CODE | 10009 | 10009 | 10009 | 10009 | 10009 |
| RESIDENTIAL UNITS | 0 | 0 | 0 | 0 | 0 |
| COMMERCIAL UNITS | 0 | 0 | 0 | 0 | 0 |
| TOTAL UNITS | 0 | 0 | 0 | 0 | 0 |
| LAND SQUARE FEET | - | - | - | - | - |
| GROSS SQUARE FEET | - | - | - | - | - |
| YEAR BUILT | 1920 | 1920 | 1920 | 1920 | 1920 |
| TAX CLASS AT TIME OF SALE | 2 | 2 | 2 | 2 | 2 |
| BUILDING CLASS AT TIME OF SALE | C6 | C6 | C6 | C6 | C6 |
| SALE PRICE | 1 | 499000 | 10 | 529500 | 423000 |
| SALE DATE | 2016-09-06 | 2017-03-10 | 2017-04-28 | 2017-06-09 | 2017-07-14 |
| bbl | 1-373-40 | 1-373-40 | 1-373-40 | 1-373-40 | 1-373-40 |
| V1 | 9718 | 9719 | 9720 | 9721 | 9722 | 9723 | 9724 | 9725 | 9726 | 9727 | ... | 9754 | 9755 | 9756 | 9757 | 9758 | 9759 | 9760 | 9761 | 9762 | 9763 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BOROUGH | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| NEIGHBORHOOD | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | ... | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST | MIDTOWN WEST |
| BUILDING CLASS CATEGORY | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | ... | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS | 45 CONDO HOTELS |
| TAX CLASS AT PRESENT | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | ... | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| BLOCK | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 | ... | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 | 1006 |
| LOT | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 | ... | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 | 1302 |
| EASE-MENT | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | ... | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| BUILDING CLASS AT PRESENT | RH | RH | RH | RH | RH | RH | RH | RH | RH | RH | ... | RH | RH | RH | RH | RH | RH | RH | RH | RH | RH |
| ADDRESS | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMER | 1335 6TH AVENUE | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | ... | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC | 1335 AVENUE OF THE AMERIC |
| APARTMENT NUMBER | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES | ... | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES | TIMES |
| ZIP CODE | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 | ... | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 | 10019 |
| RESIDENTIAL UNITS | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| COMMERCIAL UNITS | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| TOTAL UNITS | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| LAND SQUARE FEET | - | - | - | - | - | - | - | - | - | - | ... | - | - | - | - | - | - | - | - | - | - |
| GROSS SQUARE FEET | - | - | - | - | - | - | - | - | - | - | ... | - | - | - | - | - | - | - | - | - | - |
| YEAR BUILT | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 | ... | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 | 1963 |
| TAX CLASS AT TIME OF SALE | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | ... | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| BUILDING CLASS AT TIME OF SALE | RH | RH | RH | RH | RH | RH | RH | RH | RH | RH | ... | RH | RH | RH | RH | RH | RH | RH | RH | RH | RH |
| SALE PRICE | - | 10000 | - | - | - | - | - | - | - | - | ... | 45162 | 45162 | 99052 | 44612 | 45122 | 71837 | - | - | - | - |
| SALE DATE | 2017-07-07 | 2017-03-21 | 2017-03-16 | 2016-11-13 | 2016-11-11 | 2016-11-08 | 2016-10-27 | 2016-10-26 | 2016-10-26 | 2016-10-25 | ... | 2016-09-22 | 2016-09-22 | 2016-09-22 | 2016-09-22 | 2016-09-22 | 2016-09-22 | 2016-09-21 | 2016-09-20 | 2016-09-17 | 2016-09-07 |
| bbl | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | ... | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 | 1-1006-1302 |
It seems that Borough-Block-Lot is not a key in this data sets ( not unique for eacch record). This is ok, since this is a transaction data and a unit can be sold many times. For instance, bbl = '1-373-40' at V1 = 16 and V=17. Their address is the same.
On the other hand, there can be other units in the same bbl. For instance, bbl = '1-373-40' at V1 = 17, V1 = 18 and V1 = 19. All are different apartments.
To understand whether two units are the same or different in a bbl, we can befeit from address column if it is complete.
However, V1 and Borough provides a unique key in the data set.
dt[,.N,.(V1,BOROUGH)][ N > 1]
| V1 | BOROUGH | N |
|---|---|---|
| <int> | <int> | <int> |
There are 22 columns and 84548 rows.
10 columns are numeric and another 10 are character columns. However, their true type might be different, such as Sale price.
Rows are unique based on V1 and Borough, together. (V1 is not such an id column)
Some of the columns have missing values which represented as "-" or " ". ( Sale Price, Apartment Number). Therefore, missing values should be analyzed further.
"EASE-MENT" column is completely blank.
BOROUGH has only 5 distinct values. Manhattan (1), Bronx (2), Brooklyn (3), Queens (4), and Staten Island (5).
So, I'll proceed with transforming column types to analyze data further
# get rid of complete-Null column
dt[,`EASE-MENT` := NULL]
# change colnames for easy coding
cols = to.plain(colnames(dt))
colnames(dt) = cols
## make null -,_, or 0 for square feet columns
cols = setdiff(colnames(dt),"saledate")
dt[,(cols):= lapply(.SD, make_null, selected_char="-"), .SDcols = cols]
dt[,(cols):= lapply(.SD, make_null, selected_char="_"), .SDcols = cols]
sq_ft_cols = c("landsquarefeet","grosssquarefeet")
dt[,(sq_ft_cols):= lapply(.SD, make_null_0), .SDcols = sq_ft_cols]
# make character columns plain
character_cols = dt %>% keep(is.character) %>% colnames()
dt[,(character_cols):= lapply(.SD, to.plain), .SDcols = character_cols]
# make numeric the columns below
numeric_cols = c("saleprice","grosssquarefeet","landsquarefeet")
dt[,(numeric_cols):= lapply(.SD, as.numeric), .SDcols = numeric_cols]
# define an id column
dt[,idx := .GRP, .(v1,borough)]
#dt[,easement := NULL]
dt[,bbl := paste(borough,block,lot,sep="-")]
colnames(dt)
dt[,b_class_cat := as.numeric(substr(buildingclasscategory,1,2))]
dt[,b_class_present := substr(buildingclassatpresent,1,1)]
dt[,b_class_past := substr(buildingclassattimeofsale,1,1)]
#dt[,b_style := substr(buildingclassattimeofsale,2,2)]
# by using the info on https://www1.nyc.gov/site/finance/taxes/property-determining-your-assessed-value.page
dt[,taxclass_present := as.numeric(substr(taxclassatpresent,1,1))]
dt[,taxclass_present_level := ifelse(nchar(taxclassatpresent) < 2,NA,substr(taxclassatpresent,2,2))]
dt[,taxclass_past := as.numeric(substr(taxclassattimeofsale,1,1))] # includes ony integer
#dt[,taxclass_past_level := ifelse(nchar(taxclassattimeofsale) < 2,NA,substr(taxclassattimeofsale,2,2))] ALL NA
t(head(dt))
| v1 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|
| borough | 1 | 1 | 1 | 1 | 1 | 1 |
| neighborhood | alphabetcity | alphabetcity | alphabetcity | alphabetcity | alphabetcity | alphabetcity |
| buildingclasscategory | NA | NA | NA | NA | NA | NA |
| taxclassatpresent | 2a | 2 | 2 | 2b | 2a | 2 |
| block | 392 | 399 | 399 | 402 | 404 | 405 |
| lot | 6 | 26 | 39 | 21 | 55 | 16 |
| buildingclassatpresent | c2 | c7 | c7 | c4 | c2 | c4 |
| address | 153avenueb | 234east4thstreet | 197east3rdstreet | 154east7thstreet | 301east10thstreet | 516east12thstreet |
| apartmentnumber | NA | NA | NA | NA | NA | NA |
| zipcode | 10009 | 10009 | 10009 | 10009 | 10009 | 10009 |
| residentialunits | 5 | 28 | 16 | 10 | 6 | 20 |
| commercialunits | 0 | 3 | 1 | 0 | 0 | 0 |
| totalunits | 5 | 31 | 17 | 10 | 6 | 20 |
| landsquarefeet | 1633 | 4616 | 2212 | 2272 | 2369 | 2581 |
| grosssquarefeet | 6440 | 18690 | 7803 | 6794 | 4615 | 9730 |
| yearbuilt | 1900 | 1900 | 1900 | 1913 | 1900 | 1900 |
| taxclassattimeofsale | 2 | 2 | 2 | 2 | 2 | 2 |
| buildingclassattimeofsale | c2 | c7 | c7 | c4 | c2 | c4 |
| saleprice | 6625000 | NA | NA | 3936272 | 8000000 | NA |
| saledate | 2017-07-19 | 2016-12-14 | 2016-12-09 | 2016-09-23 | 2016-11-17 | 2017-07-20 |
| bbl | 1-392-6 | 1-399-26 | 1-399-39 | 1-402-21 | 1-404-55 | 1-405-16 |
| idx | 1 | 2 | 3 | 4 | 5 | 6 |
| b_class_cat | NA | NA | NA | NA | NA | NA |
| b_class_present | c | c | c | c | c | c |
| b_class_past | c | c | c | c | c | c |
| taxclass_present | 2 | 2 | 2 | 2 | 2 | 2 |
| taxclass_present_level | a | NA | NA | b | a | NA |
| taxclass_past | 2 | 2 | 2 | 2 | 2 | 2 |
skim(dt)
-- Data Summary ------------------------
Values
Name dt
Number of rows 84548
Number of columns 29
Key NULL
_______________________
Column type frequency:
character 11
numeric 17
POSIXct 1
________________________
Group variables None
-- Variable type: character ----------------------------------------------------
# A tibble: 11 x 8
skim_variable n_missing complete_rate min max empty n_unique
* <chr> <int> <dbl> <int> <int> <int> <int>
1 neighborhood 19371 0.771 4 23 0 209
2 buildingclasscategory 36244 0.571 10 36 0 33
3 taxclassatpresent 738 0.991 1 2 0 10
4 buildingclassatpresent 738 0.991 2 2 0 166
5 address 24744 0.707 4 29 0 45375
6 apartmentnumber 66304 0.216 1 11 0 3289
7 buildingclassattimeofsale 0 1 2 2 0 166
8 bbl 0 1 5 12 0 67239
9 b_class_present 738 0.991 1 1 0 25
10 b_class_past 0 1 1 1 0 25
11 taxclass_present_level 76434 0.0960 1 1 0 3
whitespace
* <int>
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
-- Variable type: numeric ------------------------------------------------------
# A tibble: 17 x 11
skim_variable n_missing complete_rate mean sd p0
* <chr> <int> <dbl> <dbl> <dbl> <dbl>
1 v1 0 1 10344. 7152. 4
2 borough 0 1 3.00 1.29 1
3 block 0 1 4237. 3568. 1
4 lot 0 1 376. 658. 1
5 zipcode 0 1 10732. 1291. 0
6 residentialunits 0 1 2.03 16.7 0
7 commercialunits 0 1 0.194 8.71 0
8 totalunits 0 1 2.25 19.0 0
9 landsquarefeet 36578 0.567 4790. 46239. 2
10 grosssquarefeet 39029 0.538 5060. 39115. 60
11 yearbuilt 0 1 1789. 537. 0
12 taxclassattimeofsale 0 1 1.66 0.819 1
13 saleprice 14561 0.828 1276456. 11405255. 0
14 idx 0 1 42274. 24407. 1
15 b_class_cat 36244 0.571 6.04 11.0 1
16 taxclass_present 738 0.991 1.65 0.817 1
17 taxclass_past 0 1 1.66 0.819 1
p25 p50 p75 p100 hist
* <dbl> <dbl> <dbl> <dbl> <chr>
1 4231 8942 15987. 26739 <U+2587><U+2586><U+2585><U+2583><U+2582>
2 2 3 4 5 <U+2586><U+2582><U+2587><U+2587><U+2582>
3 1323. 3311 6281 16322 <U+2587><U+2585><U+2582><U+2581><U+2581>
4 22 50 1001 9106 <U+2587><U+2581><U+2581><U+2581><U+2581>
5 10305 11209 11357 11694 <U+2581><U+2581><U+2581><U+2581><U+2587>
6 0 1 2 1844 <U+2587><U+2581><U+2581><U+2581><U+2581>
7 0 0 0 2261 <U+2587><U+2581><U+2581><U+2581><U+2581>
8 1 1 2 2261 <U+2587><U+2581><U+2581><U+2581><U+2581>
9 2000 2500 4000 4252327 <U+2587><U+2581><U+2581><U+2581><U+2581>
10 1416. 2000 2881 3750565 <U+2587><U+2581><U+2581><U+2581><U+2581>
11 1920 1940 1965 2017 <U+2581><U+2581><U+2581><U+2581><U+2587>
12 1 2 2 4 <U+2587><U+2587><U+2581><U+2581><U+2581>
13 225000 530000 950000 2210000000 <U+2587><U+2581><U+2581><U+2581><U+2581>
14 21138. 42274. 63411. 84548 <U+2587><U+2587><U+2587><U+2587><U+2587>
15 1 2 3 49 <U+2587><U+2581><U+2581><U+2581><U+2581>
16 1 2 2 4 <U+2587><U+2587><U+2581><U+2581><U+2581>
17 1 2 2 4 <U+2587><U+2587><U+2581><U+2581><U+2581>
-- Variable type: POSIXct ------------------------------------------------------
# A tibble: 1 x 7
skim_variable n_missing complete_rate min max
* <chr> <int> <dbl> <dttm> <dttm>
1 saledate 0 1 2016-09-01 00:00:00 2017-08-31 00:00:00
median n_unique
* <dttm> <int>
1 2017-02-28 00:00:00 364
Now, we can talk about data better.
borough has 5 values and it is distributed normally
there are 0 zipcodes, which is weird.
better to check if residentialunits + commercialunits =? totaluntit
there are 0 feet area features(!)
data has records with yearbuilt 0
taxclassatpresent and taxclassattimeofsale should have been the same type.
Since there are saleprice = 0 or NA values and I want to eliminate them, I'll try to understand the structure of the buildings first. So, I'll play with data without any focus on saleprice.
selected_cols = c("borough","zipcode","totalunits","commercialunits","residentialunits","yearbuilt","taxclass_past")
options(repr.plot.width = 5, repr.plot.height = 5, repr.plot.res = 200)
draw_corplot(dt[,.SD,.SDcols = selected_cols])
options(repr.plot.width = 10, repr.plot.height = 3, repr.plot.res = 200)
r1 = ggplot(data = dt,aes(x = log(residentialunits), fill = "orange"))+
geom_density() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
c1= ggplot(data = dt,aes(x = log(commercialunits), fill = "orange"))+
geom_density() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
grid.arrange(r1,c1,ncol = 2)
Warning message: "Removed 24783 rows containing non-finite values (stat_density)." Warning message: "Removed 79429 rows containing non-finite values (stat_density)."
selected_cols = c("borough","yearbuilt","taxclass_past","taxclass_present","commercialunits")
dt_c = copy(dt[,.SD,.SDcols = selected_cols])
dt_c = dt_c[complete.cases(dt_c)] # 738 records are deleted due to taxclass_present
options(repr.plot.width = 5, repr.plot.height = 5, repr.plot.res = 200)
draw_corplot(dt_c)
nrow(dt[taxclass_present != taxclass_past])
# only 46 records
We can consider either taxclass_present or taxclass_past as they are strongly correlated.
There is no correlation between commercial units & tax class.
negative correlation between taxclass and borough means that manhattan , for instance (borough = 1) includes mostly tax class 4 buildings.
selected_cols = c("borough","taxclass_present",'landsquarefeet','grosssquarefeet','totalunits',"commercialunits")
dt_c = copy(dt[,.SD,.SDcols = selected_cols])
dt_c = dt_c[complete.cases(dt_c)] # 27970 records are deleted
options(repr.plot.width = 5, repr.plot.height = 5, repr.plot.res = 200)
draw_corplot(dt_c)
Let's see what happens if there were no missing info in the data set.
#remove columns with NA values over 60%
s = skim(dt)
dt_complete = data.table(variable = s$skim_variable,complete_rate = s$complete_rate)
selected_cols = dt_complete[complete_rate > 0.6]$variable
dt_c = copy(dt[,.SD,.SDcols = selected_cols])
dt_c = copy(dt_c[complete.cases(dt_c)]) # 53330 records are deleted 63% is deleted
options(repr.plot.width = 10, repr.plot.height = 10, repr.plot.res = 200)
numeric_cols = dt_c %>% keep(is.numeric) %>% colnames()
draw_corplot(dt_c[,.SD,.SDcols = numeric_cols])
Now, we'll focus on saleprice. Some records are transaction between family members. Therefore, the saleprice for such transactions are 0 or very small.
Since such records cannot be used to understand the value of a construction/building, I eliminate na, 0 values and very small values.
table(dt[saleprice < 5000]$saleprice)
0 1 2 3 5 8 10 19 20 100 200 210 250
10228 134 3 2 1 1 766 1 4 90 1 1 1
300 315 373 500 501 600 750 1000 1110 1162 1175 1200 1275
1 1 1 67 1 1 1 77 1 1 1 1 1
1500 2000 2096 2200 2352 2400 2500 2510 2800 3000 3001 3012 3500
1 14 1 1 1 1 5 1 1 9 1 1 4
3750 3774 4000 4063 4500 4900
1 1 7 1 1 1
pre = nrow(dt)
dt= dt[!is.na(saleprice)]
dt= dt[saleprice != 0]
dt= dt[saleprice >200]
now = nrow(dt)
# percentage eliminated
100*(pre-now)/pre
yearbuilt
min_yearbuilt = min(dt[yearbuilt>1111]$yearbuilt)
dt[yearbuilt <=1111, yearbuilt := min_yearbuilt]
table(dt$yearbuilt)
1800 1832 1835 1844 1845 1846 1847 1849 1850 1851 1852 1854 1855 1856 1864 1865 4228 1 2 2 4 2 1 1 5 2 2 3 1 2 2 2 1870 1871 1875 1880 1881 1882 1883 1889 1890 1891 1892 1893 1894 1895 1896 1898 12 1 3 21 4 2 1 2 45 1 4 2 4 23 4 3 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1151 832 1154 23 47 16 467 57 40 65 62 2360 85 93 124 88 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 781 87 56 34 12 4035 107 124 203 249 2877 408 386 526 396 3347 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1358 191 61 22 1090 82 208 142 306 1663 176 98 6 31 834 52 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 100 193 329 2143 520 618 330 297 1378 409 392 380 359 1804 627 707 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 802 561 1002 202 164 67 147 607 123 202 174 111 424 24 113 90 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 127 269 97 102 143 138 319 319 455 289 269 222 94 100 85 73 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 88 171 102 111 143 141 184 319 292 506 792 616 815 662 419 222 2011 2012 2013 2014 2015 2016 2017 72 192 564 1000 1195 645 1
building class
# Lets see if building class changes frequently
dt[, b_class_ischanged := ifelse(b_class_present != b_class_past,1,0)]
table(dt$b_class_ischanged)
# appearently, no, then lets eliminate one of the column
0 1
58103 60
dt = dt[b_class_ischanged == 0]
dt[,c("b_class_ischanged","b_class_past") := NULL]
skim(dt)
-- Data Summary ------------------------
Values
Name dt
Number of rows 58103
Number of columns 28
Key NULL
_______________________
Column type frequency:
character 10
numeric 17
POSIXct 1
________________________
Group variables None
-- Variable type: character ----------------------------------------------------
# A tibble: 10 x 8
skim_variable n_missing complete_rate min max empty n_unique
* <chr> <int> <dbl> <int> <int> <int> <int>
1 neighborhood 13934 0.760 4 23 0 208
2 buildingclasscategory 27945 0.519 10 36 0 33
3 taxclassatpresent 0 1 1 2 0 9
4 buildingclassatpresent 0 1 2 2 0 152
5 address 17001 0.707 6 29 0 32728
6 apartmentnumber 45186 0.222 1 11 0 2615
7 buildingclassattimeofsale 0 1 2 2 0 152
8 bbl 0 1 5 12 0 45509
9 b_class_present 0 1 1 1 0 24
10 taxclass_present_level 53197 0.0844 1 1 0 3
whitespace
* <int>
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
-- Variable type: numeric ------------------------------------------------------
# A tibble: 17 x 11
skim_variable n_missing complete_rate mean sd p0
* <chr> <int> <dbl> <dbl> <dbl> <dbl>
1 v1 0 1 10293. 7076. 4
2 borough 0 1 2.94 1.33 1
3 block 0 1 4154. 3573. 1
4 lot 0 1 383. 666. 1
5 zipcode 0 1 10766. 973. 0
6 residentialunits 0 1 1.72 14.2 0
7 commercialunits 0 1 0.165 9.96 0
8 totalunits 0 1 1.90 17.5 0
9 landsquarefeet 28558 0.508 4617. 46644. 33
10 grosssquarefeet 29543 0.492 4562. 33962. 120
11 yearbuilt 0 1 1942. 49.2 1800
12 taxclassattimeofsale 0 1 1.63 0.703 1
13 saleprice 0 1 1505757. 12445245. 210
14 idx 0 1 41485. 24944. 1
15 b_class_cat 27945 0.519 4.69 8.81 1
16 taxclass_present 0 1 1.63 0.703 1
17 taxclass_past 0 1 1.63 0.703 1
p25 p50 p75 p100 hist
* <dbl> <dbl> <dbl> <dbl> <chr>
1 4259 9023 15755 26738 <U+2587><U+2586><U+2585><U+2583><U+2582>
2 2 3 4 5 <U+2586><U+2582><U+2587><U+2587><U+2582>
3 1278 3167 6111 16319 <U+2587><U+2585><U+2582><U+2581><U+2581>
4 21 50 1002 9106 <U+2587><U+2581><U+2581><U+2581><U+2581>
5 10301 11207 11357 11694 <U+2581><U+2581><U+2581><U+2581><U+2587>
6 0 1 2 1844 <U+2587><U+2581><U+2581><U+2581><U+2581>
7 0 0 0 2261 <U+2587><U+2581><U+2581><U+2581><U+2581>
8 0 1 2 2261 <U+2587><U+2581><U+2581><U+2581><U+2581>
9 2000 2500 4000 4252327 <U+2587><U+2581><U+2581><U+2581><U+2581>
10 1360 1872 2664 3750565 <U+2587><U+2581><U+2581><U+2581><U+2581>
11 1920 1940 1967 2017 <U+2582><U+2581><U+2587><U+2587><U+2585>
12 1 2 2 4 <U+2587><U+2587><U+2581><U+2581><U+2581>
13 380000 635000 1080000 2210000000 <U+2587><U+2581><U+2581><U+2581><U+2581>
14 18946. 41247 63286. 84548 <U+2587><U+2587><U+2586><U+2587><U+2587>
15 1 2 3 49 <U+2587><U+2581><U+2581><U+2581><U+2581>
16 1 2 2 4 <U+2587><U+2587><U+2581><U+2581><U+2581>
17 1 2 2 4 <U+2587><U+2587><U+2581><U+2581><U+2581>
-- Variable type: POSIXct ------------------------------------------------------
# A tibble: 1 x 7
skim_variable n_missing complete_rate min max
* <chr> <int> <dbl> <dttm> <dttm>
1 saledate 0 1 2016-09-01 00:00:00 2017-08-31 00:00:00
median n_unique
* <dttm> <int>
1 2017-03-01 00:00:00 335
Also, we know that taxclass_present and taxclass_past are highly correlated. taxclass_present has about 500 NA records. I believe these are demolished buildings. So, I decided to eliminate taxclass_past completely and only na values of taxclass_present
dt[,c("taxclass_present_level","taxclass_past") := NULL]
dt = dt[!is.na(taxclass_present)] #593 records are eliminated
nrow(dt)
colSums(is.na(dt))
options(repr.plot.width = 5, repr.plot.height = 2, repr.plot.res = 200)
mice_plot <- aggr(dt, col=c('navyblue','yellow'),
numbers=TRUE, sortVars=TRUE,
labels=names(dt), cex.axis=.7,
gap=3, ylab=c("Missing data","Pattern"))
mice_plot
Warning message in plot.aggr(res, ...): "not enough vertical space to display frequencies (too many combinations)"
Variables sorted by number of missings:
Variable Count
apartmentnumber 0.7776879
grosssquarefeet 0.5084591
landsquarefeet 0.4915065
buildingclasscategory 0.4809562
b_class_cat 0.4809562
address 0.2926011
neighborhood 0.2398155
v1 0.0000000
borough 0.0000000
taxclassatpresent 0.0000000
block 0.0000000
lot 0.0000000
buildingclassatpresent 0.0000000
zipcode 0.0000000
residentialunits 0.0000000
commercialunits 0.0000000
totalunits 0.0000000
yearbuilt 0.0000000
taxclassattimeofsale 0.0000000
buildingclassattimeofsale 0.0000000
saleprice 0.0000000
saledate 0.0000000
bbl 0.0000000
idx 0.0000000
b_class_present 0.0000000
taxclass_present 0.0000000
Missings in variables:
Variable Count
neighborhood 13934
buildingclasscategory 27945
address 17001
apartmentnumber 45186
landsquarefeet 28558
grosssquarefeet 29543
b_class_cat 27945
neighborhood is such a nice column to take a reference in order to impute missing values in other columns.However, almost 25% ofthis column has missing values.
buildingclasscategory is only 57% complete. Therefore,it is better to ignore it.
Almost 30% of address is missing. No need to impute it.
Apartmentnumber has much more missing values.
landssquarefeet and grosssquarefeet have too many missing data points.
#nothing can be drawn from apartmentnumber, so we can delete it peacefully.
dt[,.N,.(apartmentnumber)][order(-N)] %>% head(5)
| apartmentnumber | N |
|---|---|
| <chr> | <int> |
| NA | 45186 |
| 3b | 217 |
| 3a | 216 |
| 2b | 207 |
| 2a | 205 |
dt[,c("apartmentnumber") := NULL]
Other Features
I'll try to extract some info from features listed below.
s = skim(dt)
dt_complete = data.table(variable = s$skim_variable,complete_rate = s$complete_rate)[complete_rate < 1]
dt_complete[order(complete_rate)]
| variable | complete_rate |
|---|---|
| <chr> | <dbl> |
| grosssquarefeet | 0.4915409 |
| landsquarefeet | 0.5084935 |
| buildingclasscategory | 0.5190438 |
| b_class_cat | 0.5190438 |
| address | 0.7073989 |
| neighborhood | 0.7601845 |
Building class and building category seem to be relevant. There is no such a 1-1 kind of mapping between themm. Yet, for instance, b_class = a is always category 1.
In the beginning I though I might impute building category by using b_class_present. However most missing values of category are c,d, and r of b_class. These classes do not have enoght number of observations with complete category info.
Apart from that, only b_class_cat 1 and 2 have a meaningful presence in the data set and b_class_present can cover this as well.
So, I'll ignore building category in the calculations in the further steps.
b_class_freq = dt[,.N,.(b_class_present,b_class_cat)]
b_class_freq[, perc := round(N / sum(N),2)]
b_class_freq[order(b_class_present)]
b_class_freq[order(b_class_present)][ perc > 0.05]
| b_class_present | b_class_cat | N | perc |
|---|---|---|---|
| <chr> | <dbl> | <int> | <dbl> |
| a | 1 | 12491 | 0.21 |
| b | 2 | 9459 | 0.16 |
| c | NA | 4250 | 0.07 |
| c | 3 | 2327 | 0.04 |
| d | NA | 11719 | 0.20 |
| e | 30 | 167 | 0.00 |
| f | 27 | 106 | 0.00 |
| g | 29 | 273 | 0.00 |
| g | NA | 43 | 0.00 |
| h | 25 | 8 | 0.00 |
| h | 26 | 93 | 0.00 |
| i | 32 | 28 | 0.00 |
| j | 34 | 4 | 0.00 |
| k | 22 | 477 | 0.01 |
| l | 23 | 21 | 0.00 |
| m | 37 | 62 | 0.00 |
| n | 38 | 14 | 0.00 |
| o | 21 | 221 | 0.00 |
| p | 35 | 16 | 0.00 |
| q | 36 | 6 | 0.00 |
| r | NA | 11546 | 0.20 |
| r | 17 | 1111 | 0.02 |
| r | 46 | 72 | 0.00 |
| r | 4 | 1225 | 0.02 |
| r | 43 | 239 | 0.00 |
| r | 44 | 264 | 0.00 |
| r | 28 | 14 | 0.00 |
| r | 45 | 81 | 0.00 |
| r | 42 | 4 | 0.00 |
| r | 49 | 10 | 0.00 |
| r | 48 | 12 | 0.00 |
| r | 11 | 1 | 0.00 |
| s | NA | 327 | 0.01 |
| s | 2 | 436 | 0.01 |
| s | 1 | 218 | 0.00 |
| t | 39 | 1 | 0.00 |
| v | 31 | 185 | 0.00 |
| v | 5 | 485 | 0.01 |
| w | 33 | 26 | 0.00 |
| y | 40 | 1 | 0.00 |
| z | NA | 60 | 0.00 |
| b_class_present | b_class_cat | N | perc |
|---|---|---|---|
| <chr> | <dbl> | <int> | <dbl> |
| a | 1 | 12491 | 0.21 |
| b | 2 | 9459 | 0.16 |
| c | NA | 4250 | 0.07 |
| d | NA | 11719 | 0.20 |
| r | NA | 11546 | 0.20 |
Nevertheless, I just want to explore building category more.
dt_tree = dt[,.(M = mean(saleprice, na.rm = TRUE), N = .N) ,.(buildingclasscategory)]
options(repr.plot.width = 5, repr.plot.height = 3, repr.plot.res = 200)
ggplot(dt_tree
, aes(area = M, fill = N, label = buildingclasscategory)) +
geom_treemap() +
geom_treemap_text(colour = "white", place = "topleft", reflow = T)
Warning message: "Removed 1 rows containing missing values (geom_treemap_text)."
So, luxuryhotels have the highet mean but there are only 8 records. Light Blue colored cell is on the top right of the treemap.
It really seems that we cannot benefit from building category regarding price prediction.
#dt[,saleprice_group := Hmisc::cut2(saleprice,g = 10)]
#
#options(repr.plot.width = 12, repr.plot.height = 8, repr.plot.res = 200)
#ggplot(data = dt, aes(y = saleprice, x = as.factor(b_class_past), fill =as.factor(b_class_past))) +
# geom_boxplot() +
# facet_wrap(~ saleprice_group,scales = "free", ncol = 2)
#
### b_class_cat'ı b_class_present ile doldurabiliriz.
b_groups = dt[,.N,.(b_class_present)][order(-N)]
b_groups[, perc := N/sum(N)][order(-perc)]
| b_class_present | N | perc |
|---|---|---|
| <chr> | <int> | <dbl> |
| r | 14579 | 0.25091647591 |
| a | 12491 | 0.21498029362 |
| d | 11719 | 0.20169354422 |
| b | 9459 | 0.16279710170 |
| c | 6577 | 0.11319553207 |
| s | 981 | 0.01688380979 |
| v | 670 | 0.01153124624 |
| k | 477 | 0.00820955889 |
| g | 316 | 0.00543861763 |
| o | 221 | 0.00380359018 |
| e | 167 | 0.00287420615 |
| f | 106 | 0.00182434642 |
| h | 101 | 0.00173829234 |
| m | 62 | 0.00106707055 |
| z | 60 | 0.00103264892 |
| i | 28 | 0.00048190283 |
| w | 26 | 0.00044748120 |
| l | 21 | 0.00036142712 |
| p | 16 | 0.00027537304 |
| n | 14 | 0.00024095141 |
| q | 6 | 0.00010326489 |
| j | 4 | 0.00006884326 |
| y | 1 | 0.00001721082 |
| t | 1 | 0.00001721082 |
dt[, b_class_group := encode_grouping(b_class_present, Q = 6,name_for_other = "other")]
dt[,.N,.(b_class_group)]
| b_class_group | N |
|---|---|
| <chr> | <int> |
| c | 6577 |
| d | 11719 |
| r | 14579 |
| other | 3278 |
| a | 12491 |
| b | 9459 |
Maybe we can obtain some info from address column. Let's see.
address_focus = dt$address
address_focus[str_detect(address_focus, "street")] = "street"
address_focus[str_detect(address_focus, "avenue")] = "avenue"
address_focus[str_detect(address_focus, "square")] = "square"
address_focus[str_detect(address_focus, "highway")] = "highway"
address_focus[str_detect(address_focus, "boulevard")] = "boulevard"
address_focus[str_detect(address_focus, "centralpark")] = "centralpark"
address_focus[str_detect(address_focus, "broadway")] = "broaadway"
address_focus[str_detect(address_focus, "road")] = "road"
address_focus[!address_focus %in% c("street","avenue","square","highway","boulevard","centralpark","broaadway","road")] = "other"
table(address_focus)
address_focus
avenue boulevard broaadway centralpark highway other
12094 386 342 331 38 23774
road square street
1136 45 19957
## lets try to simplify the address column
address_focus = dt$address
address_focus[str_detect(address_focus, "street")] = "street"
address_focus[str_detect(address_focus, "avenue")] = "avenue"
address_focus[!address_focus %in% c("street","avenue")] = "other"
table(address_focus)
dt$address = address_focus
address_focus avenue other street 12094 26052 19957
selected_cols = c("saleprice","totalunits","commercialunits")
options(repr.plot.width = 8, repr.plot.height = 3, repr.plot.res = 200)
draw_boxplot(dt[saleprice < 2000000 & totalunits< 10 & commercialunits < 2],id_col = "address", selected_cols)
Warning message in melt.data.table(., id.vars = id_col): "'measure.vars' [saleprice, totalunits, commercialunits] are not all of the same type. By order of hierarchy, the molten data value column will be of type 'double'. All measure variables not of type 'double' will be coerced too. Check DETAILS in ?melt.data.table for more on coercion."
new address info can be useful in prediction of small house prices.
The size of the rectangules implies the number of data points, while the color shows the mean sale price in the neighborhood.
dt_tree = dt[saleprice < 10000000,.(N = .N, M = mean(saleprice,na.rm = TRUE)),.(borough,neighborhood)]
options(repr.plot.width = 10, repr.plot.height = 5, repr.plot.res = 200)
ggplot(dt_tree
, aes(area = N, fill = M, label = neighborhood,
subgroup = borough)) +
geom_treemap() +
geom_treemap_subgroup_border() +
geom_treemap_subgroup_text(place = "centre", grow = T, alpha = 0.5, colour =
"black", fontface = "italic", min.size = 0) +
geom_treemap_text(colour = "white", place = "topleft", reflow = T)
Warning message: "Removed 5 rows containing missing values (geom_treemap_text)."
dt_tree = dt[saleprice < 10000000,.(N = .N, M = mean(saleprice,na.rm = TRUE)),.(borough,neighborhood)]
for(i in c(1:5)){
g = ggplot(dt_tree[borough == i]
, aes(area = N, fill = M, label = neighborhood,subgroup = borough)) +
geom_treemap() +
geom_treemap_subgroup_border() +
geom_treemap_subgroup_text(place = "centre", grow = T, alpha = 0.5, colour =
"black", fontface = "italic", min.size = 0) +
geom_treemap_text(colour = "white", place = "topleft", reflow = T)
assign(paste0("g_",i),g)
}
options(repr.plot.width = 15, repr.plot.height = 5, repr.plot.res = 200)
gridExtra::grid.arrange(g_1,g_2,g_3,g_4,g_5, nrow = 2)
Warning message: "Removed 1 rows containing missing values (geom_treemap_text)." Warning message: "Removed 1 rows containing missing values (geom_treemap_text)." Warning message: "Removed 1 rows containing missing values (geom_treemap_text)." Warning message: "Removed 1 rows containing missing values (geom_treemap_text)." Warning message: "Removed 1 rows containing missing values (geom_treemap_text)."
midtowncbd includes the terrific record with 210000000000 saleprice.
t(dt[saleprice > 2000000000])
loc_cols = c('borough','block','lot',"zipcode")
options(repr.plot.width = 10, repr.plot.height = 3, repr.plot.res = 200)
draw_barplot(dt,loc_cols,bin_number = 10,ncol = 4)
draw_barplot(dt,loc_cols,group_number = 10,ncol = 4)
Warning message: "attributes are not identical across measure variables; they will be dropped" Warning message in min(xx[xx > upper]): "no non-missing arguments to min; returning Inf" Warning message: "attributes are not identical across measure variables; they will be dropped"
selected_cols = c('residentialunits','commercialunits','totalunits','landsquarefeet','grosssquarefeet','yearbuilt')
options(repr.plot.width = 10, repr.plot.height = 3, repr.plot.res = 200)
draw_barplot(dt,selected_cols,bin_number = 10,ncol = 6)
draw_barplot(dt,selected_cols,group_number = 10,ncol = 6)
Warning message: "attributes are not identical across measure variables; they will be dropped" Warning message: "attributes are not identical across measure variables; they will be dropped"
dt_c = copy(dt[ ,.(residential_group = Hmisc::cut2(residentialunits, g = 10),
commerical_group = Hmisc::cut2(commercialunits, g = 10),
totalunit_group = Hmisc::cut2(totalunits, g = 10),
landsquarefeet_group = Hmisc::cut2(landsquarefeet, g = 10),
grosssquarefeet_group = Hmisc::cut2(grosssquarefeet, g = 10),
grosssquarefeet_log_group = Hmisc::cut2(log(grosssquarefeet), g = 10),
grosssquarefeet_log = log(grosssquarefeet),
yearbuilt_group = Hmisc::cut2(yearbuilt, g = 10),
saleprice,
saleprice_log = log(saleprice),
saleprice_sqrt = sqrt(saleprice),
totalunits,
commercialunits,
yearbuilt,
grosssquarefeet,
zipcode,
address,
borough = as.factor(borough),
taxclass_present =as.factor(taxclass_present),
b_class_present = as.factor(b_class_present),
b_class_group)])
dt_c[, saleprice_wo := tend_outliers_keep(saleprice, sigma = 3),.(borough)]
dt_c[, saleprice_wo_log := log(saleprice_wo)]
dt_c[, saleprice_log_wo := tend_outliers_keep(saleprice_log, sigma = 3),.(borough)]
price_summaries = dt_c[, as.list(summary(saleprice)), by = borough]
price_summaries
price_summaries = dt_c[, as.list(summary(saleprice_wo)), by = borough]
price_summaries
| borough | Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|---|
| <fct> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| 1 | 373 | 665000 | 1150000.0 | 3327982.1 | 2425000 | 2210000000 |
| 2 | 250 | 226000 | 405300.0 | 825425.6 | 575000 | 110000000 |
| 3 | 210 | 456246 | 765000.0 | 1306216.2 | 1261239 | 345000000 |
| 4 | 300 | 295000 | 499994.5 | 750476.6 | 780000 | 257500000 |
| 5 | 500 | 335000 | 468468.0 | 553102.3 | 600000 | 122000000 |
| borough | Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|---|
| <fct> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| 1 | 373 | 665000 | 1150000.0 | 2364344.3 | 2425000 | 16971406 |
| 2 | 250 | 226000 | 405300.0 | 568088.9 | 575000 | 3687222 |
| 3 | 210 | 456246 | 765000.0 | 1036736.0 | 1261239 | 4804451 |
| 4 | 300 | 295000 | 499994.5 | 603108.1 | 780000 | 2449214 |
| 5 | 500 | 335000 | 468468.0 | 490229.5 | 600000 | 1409392 |
Saleprice quartiles of each borough seems different. Especially look at borought 1 and 2.
dt_molten = dt_c[,.(borough,saleprice,saleprice_log,saleprice_log_wo,saleprice_wo,saleprice_wo_log,saleprice_sqrt)] %>%
melt.data.table(id.vars = c("borough"))
options(repr.plot.width = 10, repr.plot.height = 10, repr.plot.res = 200)
ggplot(data = dt_molten ,aes(x = value, fill = as.factor(borough)))+
facet_wrap(variable ~ borough, scales ="free", ncol = 5) +
geom_density() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
id_cols = c("residential_group","commerical_group","totalunit_group","borough","taxclass_present","b_class_present","b_class_group","grosssquarefeet_group","grosssquarefeet_log_group","landsquarefeet_group","yearbuilt_group","address")
selected_cols = c('saleprice_log_wo')
options(repr.plot.width = 6, repr.plot.height = 3, repr.plot.res = 200)
for(i in id_cols){
print(draw_boxplot(dt_c,id_col = i, selected_cols))
}
dt_c[,totalunits_group := ifelse(totalunits<4,totalunits,4)]
dt_c[,commercialunits_group := ifelse(commercialunits<2,commercialunits,2)]
dt_c[,yearbuilt_group := Hmisc::cut2(yearbuilt, g = 5)]
dt_c[,grosssquarefeet_log_group := Hmisc::cut2(grosssquarefeet_log, g = 5)]
dt_c[,saleprice_log_wo_group := Hmisc::cut2(saleprice_log_wo,g = 5),.(borough)]
options(repr.plot.width = 12, repr.plot.height = 6, repr.plot.res = 200)
ggplot(data = dt_c, aes(y = saleprice_log_wo, fill = borough)) +
geom_boxplot() +
facet_grid(borough ~ totalunits_group,scales = "free")
options(repr.plot.width = 12, repr.plot.height = 6, repr.plot.res = 200)
ggplot(data = dt_c, aes(y = saleprice_log_wo, fill = borough)) +
geom_boxplot() +
facet_grid(borough ~ commercialunits_group,scales = "free")
options(repr.plot.width = 12, repr.plot.height = 6, repr.plot.res = 200)
ggplot(data = dt_c, aes(y = saleprice_log_wo, fill = yearbuilt_group)) +
geom_boxplot() +
facet_grid(borough ~ yearbuilt_group,scales = "free")
options(repr.plot.width = 12, repr.plot.height = 6, repr.plot.res = 200)
ggplot(dt_c, aes(x = yearbuilt, y = saleprice_log_wo, fill = as.factor(totalunits_group), color = as.factor(totalunits_group))) + geom_point() + facet_wrap(~borough)
options(repr.plot.width = 12, repr.plot.height = 6, repr.plot.res = 200)
ggplot(dt_c, aes(x = yearbuilt, y = saleprice_log_wo, fill = as.factor(grosssquarefeet_log_group), color = as.factor(grosssquarefeet_log_group))) + geom_point() + facet_wrap(~borough)
dt_c[, yearbuilt_group := ifelse(yearbuilt < 1900,1,ifelse(yearbuilt < 1950,2,3))]
options(repr.plot.width = 12, repr.plot.height = 6, repr.plot.res = 200)
ggplot(data = dt_c, aes(y = saleprice_log_wo, fill = borough)) +
geom_boxplot() +
facet_grid(borough ~ yearbuilt_group,scales = "free")
Not an obvious finding regarding yearbuilt. But it can be connected with other features.
ggplot(dt_c, aes(x = yearbuilt, y = totalunit_group, fill = as.factor(grosssquarefeet_log_group), color = as.factor(grosssquarefeet_log_group))) + geom_point() + facet_wrap(~borough)
ggplot(data = dt_c ,aes(x = grosssquarefeet_log, fill = as.factor(borough)))+
facet_wrap( ~ borough, scales ="free") +
geom_density() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
Warning message: "Removed 29543 rows containing non-finite values (stat_density)."
#tempData <- mice(dt_c[,.(borough,yearbuilt_group,address,grosssquarefeet,zipcode)] ,m=5,maxit=50,meth='pmm',seed=500)
#completedData <- complete(tempData,1)
dt_c[,grosssquarefeet_log_filled := grosssquarefeet_log]
dt_c[,grosssquarefeet_log_filled := fill_missing(grosssquarefeet_log_filled,method = "mean"),.(borough,totalunit_group)]
#dt_c[,grosssquarefeet_filled := completedData$grosssquarefeet]
dt_c[,grosssquarefeet_log_group := Hmisc::cut2(grosssquarefeet_log_filled, g = 5)]
table(dt_c$grosssquarefeet_log_group)
[4.79, 7.27) [7.27, 7.54) [7.54, 8.20) [8.20, 8.89) [8.89,15.14]
12305 10960 13468 12992 8378
options(repr.plot.width = 12, repr.plot.height = 6, repr.plot.res = 200)
ggplot(dt_c, aes(x = yearbuilt, y = saleprice_log_wo, fill = as.factor(grosssquarefeet_log_group), color = as.factor(grosssquarefeet_log_group))) + geom_point() + facet_wrap(~borough)
options(repr.plot.width = 7, repr.plot.height = 6, repr.plot.res = 200)
ggplot(data = dt_c, aes(y = saleprice_log_wo, fill = grosssquarefeet_log_group)) +
geom_boxplot() +
facet_grid(borough ~ grosssquarefeet_log_group,scales = "free")
dt[, saleprice_log := log(saleprice)]
dt[, saleprice_wo := tend_outliers_keep(saleprice, sigma = 3),.(borough)]
dt[, saleprice_wo_log := log(saleprice_wo)]
dt[, saleprice_log_wo := tend_outliers_keep(saleprice_log, sigma = 3),.(borough)]
dt[, yearbuilt_group := ifelse(yearbuilt < 1900,1,ifelse(yearbuilt < 1950,2,3))]
dt[, totalunits_group := ifelse(totalunits<4,totalunits,4)]
dt[, commercialunits_group := ifelse(commercialunits<2,commercialunits,2)]
dt[,grosssquarefeet_log_filled := log(grosssquarefeet)]
dt[,grosssquarefeet_log_filled := fill_missing(grosssquarefeet_log_filled,method = "mean"),.(borough,totalunits_group)]
dt[,grosssquarefeet_log_group := Hmisc::cut2(grosssquarefeet_log_filled, g = 5)]
dt[, commercialunits_sqrt := sqrt(commercialunits)]
dt[, residentialunits_sqrt := sqrt(residentialunits)]
dt[,residentialunits_group := Hmisc::cut2(residentialunits_sqrt, g = 5)]
dt[,residentialunits_group := encode_numeric(residentialunits_group)]
options(repr.plot.width = 10, repr.plot.height = 2, repr.plot.res = 200)
ggplot(data = dt ,aes(x = residentialunits_sqrt, fill = as.factor(borough)))+
facet_wrap( ~ borough, scales ="free", ncol = 5 ) +
geom_density() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
building_cols = c("residentialunits_group","commercialunits_group","grosssquarefeet_log_filled")
dt_cluster = copy(scale(dt[,.SD,.SDcols = building_cols ]))
set.seed(0)
building_clusters <- kmeans(dt_cluster, centers = 5, nstart = 25)
table(building_clusters$cluster)
building_clusters
1 2 3 4 5
2730 11920 15728 7376 20349
K-means clustering with 5 clusters of sizes 2730, 11920, 15728, 7376, 20349
Cluster means:
residentialunits_group commercialunits_group grosssquarefeet_log_filled
1 -0.4883702 4.2341652 0.538697838
2 -1.1983317 -0.2088750 0.129745087
3 1.0598819 -0.2088750 0.001321825
4 -0.8902921 -0.2079576 1.954910306
5 0.2709881 -0.2088750 -0.857900369
Clustering vector:
[1] 2 2 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[37] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3
[73] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[109] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 3 3 3 3 3 3 3 3 3 1 1 4 4 4 4 4 4 1 2
[145] 2 3 1 2 3 1 2 2 2 1 2 2 2 2 1 1 1 2 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[181] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[217] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[253] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[289] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[325] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[361] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3
[397] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[433] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[469] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[505] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[541] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[577] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[613] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3 3 3 3 3 3 3
[649] 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[685] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[721] 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 1 2 2 2 2 2 2 2 2 2 1 1 3 3
[757] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1
[793] 1 1 2 2 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4
[829] 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[865] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[901] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[937] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[973] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1009] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1045] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 1 1 4
[1081] 4 4 4 1 1 1 1 2 2 2 2 2 2 2 1 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1117] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1153] 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1189] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1225] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1261] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1297] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1333] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 1 1
[1369] 1 4 1 1 1 1 3 1 2 2 2 2 1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1405] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1441] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3
[1477] 3 3 3 3 3 3 3 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 4 4 2 1 4 4 4 4 4 4 4 4
[1513] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1549] 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4 2 2 2 2 2 1 1
[1585] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1621] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1657] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1693] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1729] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1765] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1801] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1837] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1873] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1909] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1945] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1981] 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 1 4 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 4 4 4 4
[2017] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2053] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2089] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2125] 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[2161] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[2197] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[2233] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[2269] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[2305] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3
[2341] 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 2 2 1 3 3 3 2 3 3 2 1 2 1
[2377] 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2413] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2449] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2485] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2521] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2557] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2593] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2629] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[2665] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[2701] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 4
[2737] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 3 3 3 3 1 3 1 1 2 4 4 2 2 1 4 4 4 4
[2773] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2809] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2845] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2881] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2917] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2953] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[2989] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3025] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3061] 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[3097] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3
[3133] 3 3 3 3 3 3 3 3 3 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3169] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3205] 4 4 4 4 4 4 1 1 1 1 1 1 2 2 2 2 2 2 5 5 5 1 3 1 1 1 1 1 3 3 3 3 1 3 3 2
[3241] 2 2 2 1 2 2 4 1 2 4 4 2 1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3277] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3313] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3349] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3385] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3421] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3457] 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[3493] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[3529] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[3565] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[3601] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[3637] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 1 4 4 4 4 4 4 4 4 4 4 4 4 4
[3673] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 4 4 2 2 3 3 3 3 3 3 3 3
[3709] 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 3 3 1
[3745] 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2
[3781] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 4 1 2 4 1 1 2 2 4 4 4 4 4 4 4 4 4 4 4 1 1
[3817] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3853] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[3889] 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[3925] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[3961] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[3997] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4033] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4069] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4105] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4141] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4177] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3 3 3 3 3 3
[4213] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[4249] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 4 1 1 4 4 4 4 4 1 4 1 4
[4285] 1 1 1 1 1 4 1 1 1 2 2 2 5 1 3 2 2 2 1 4 1 1 1 1 2 4 2 1 1 4 4 4 4 1 2 2
[4321] 2 2 2 2 2 1 1 1 1 2 2 1 2 2 2 2 1 2 2 1 1 1 4 4 4 1 1 4 2 1 1 1 1 2 4 4
[4357] 2 4 4 1 2 2 2 2 4 2 4 2 1 1 2 4 2 2 1 4 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[4393] 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4429] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4465] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4
[4501] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 4 4 4 4 4 1 2 3 3 3 3 5 5 3 3 3 3
[4537] 3 3 3 3 3 3 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 4 4 4 4 4 4 4 4
[4573] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3
[4609] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 4 1 4 1 5 3 2 2 4 4 1 1 4 4 4 4
[4645] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 1 5 5 3 4 1 1 4 4 4 4 4 4 4 4 1 4 4
[4681] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[4717] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 1
[4753] 1 1 1 2 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 1 1 1 1 1 1 4 3 3 1 2
[4789] 2 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3
[4825] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4861] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4897] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4933] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[4969] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[5005] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5041] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 2 2 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3
[5077] 3 3 3 3 3 3 1 4 1 1 1 1 1 1 2 2 1 1 1 4 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5113] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5149] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5185] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5221] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3
[5257] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3 3 3 3 1
[5293] 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 4 1 4 1 2 2 2 2 2 3 3 4 4 2 1 1 4 4 2 2 2
[5329] 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5365] 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[5401] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[5437] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
[5473] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5509] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[5545] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[5581] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 2 2 2
[5617] 2 2 2 2 2 2 3 3 3 2 2 1 2 2 2 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5653] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5689] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5725] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5761] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5797] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5833] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5869] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5905] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5941] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[5977] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6013] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6049] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6085] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6121] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6157] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6193] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6229] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6265] 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6301] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6337] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6373] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6409] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6445] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6481] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6517] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6553] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6589] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 4 4 4
[6625] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1
[6661] 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 1 1 4 4
[6697] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6733] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6769] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[6805] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6841] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6877] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6913] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6949] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[6985] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7021] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7057] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7093] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7129] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7165] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7201] 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1
[7237] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[7273] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[7309] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[7345] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7381] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7417] 5 5 3 1 2 2 2 1 2 1 1 2 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7453] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7489] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7525] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7561] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7597] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7633] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7669] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3
[7705] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7741] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7777] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7813] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[7849] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 3 3 4 4 4 4 4 4 4 4 4
[7885] 4 4 4 1 1 1 1 1 1 1 1 1 4 4 4 4 2 2 2 2 2 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4
[7921] 4 4 4 4 4 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7957] 4 4 4 4 4 4 4 4 4 4 4 3 1 3 1 1 1 1 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[7993] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[8029] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[8065] 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8101] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8137] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 1
[8173] 1 1 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 4 4 4 4 4 4
[8209] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[8245] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3
[8281] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8317] 3 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3
[8353] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8389] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8425] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8461] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8497] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8533] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8569] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8605] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8641] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8677] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8713] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8749] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[8785] 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1
[8821] 1 1 4 4 4 4 4 4 4 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 1 3
[8857] 3 3 3 5 5 3 3 3 3 3 3 3 3 3 2 2 1 2 2 1 1 1 1 1 1 1 1 2 2 2 1 1 4 4 4 4
[8893] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4
[8929] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[8965] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9001] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9037] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9073] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9109] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4
[9145] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9181] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9217] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9253] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9289] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9325] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9361] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9397] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9433] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9469] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9505] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9541] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9577] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4
[9613] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9649] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9685] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[9721] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[9757] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[9793] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[9829] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[9865] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[9901] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[9937] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[9973] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[10009] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[10045] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[10081] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 4 4 4 4 4
[10117] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10153] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10189] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10225] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10261] 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 4 1 2 2 2 2 2 2 2 2 2 2 2 2
[10297] 2 2 2 3 3 3 3 1 1 3 3 3 3 3 3 5 3 3 2 2 2 2 2 1 2 2 1 1 2 1 2 2 2 1 1 1
[10333] 2 2 2 2 2 2 2 2 2 1 1 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10369] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10405] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10441] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10477] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10513] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10549] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10585] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10621] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10657] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10693] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10729] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10765] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10801] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10837] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10873] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10909] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[10945] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4
[10981] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11017] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11053] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11089] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11125] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11161] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11197] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11233] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11269] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11305] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11341] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11377] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11413] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11449] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11485] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11521] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[11557] 3 3 1 3 3 3 3 3 3 3 3 3 3 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1
[11593] 1 1 2 2 2 2 4 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3
[11629] 3 3 3 3 3 3 3 3 2 3 3 3 1 3 3 2 2 2 2 2 2 1 2 2 2 4 4 4 4 4 4 4 4 4 4 4
[11665] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11701] 4 4 4 4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11737] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11773] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11809] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11845] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11881] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11917] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11953] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[11989] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12025] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3
[12061] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12097] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12133] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12169] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12205] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12241] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12277] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12313] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12349] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12385] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12421] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[12457] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4
[12493] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12529] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12565] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12601] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12637] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 2 2 2 2 2
[12673] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[12709] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 2 2 2 3 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 1 4 4
[12745] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12781] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12817] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12853] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12889] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12925] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12961] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[12997] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13033] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13069] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4
[13105] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13141] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13177] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3
[13213] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[13249] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[13285] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[13321] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[13357] 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13393] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 2 2 3 3 2 2 1 2 2 2 2 2 4 4 4 4
[13429] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13465] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13501] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13537] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13573] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3
[13609] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[13645] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4
[13681] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 2 2 2 3 3 3 2 2 4 2 2 2 2 2 2 1 2
[13717] 4 4 4 4 1 4 1 4 4 4 4 4 1 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13753] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13789] 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 2 3 3 4 4 4 4
[13825] 2 2 4 4 1 1 4 4 4 1 4 1 1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13861] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13897] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13933] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[13969] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[14005] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[14041] 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 4
[14077] 5 5 5 5 5 5 3 1 3 1 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 4 1 2 5 5
[14113] 5 1 1 1 1 5 1 1 5 1 5 5 5 5 5 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[14149] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[14185] 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[14221] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3
[14257] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[14293] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[14329] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2
[14365] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5
[14401] 5 5 5 5 5 5 5 4 2 2 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5
[14437] 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3
[14473] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5
[14509] 5 5 4 4 4 4 2 2 2 2 4 4 2 2 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[14545] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1
[14581] 5 5 1 1 5 5 5 3 3 3 3 3 3 1 1 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 5 5 2
[14617] 1 1 2 1 4 2 4 1 4 2 1 4 5 1 1 1 1 1 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5
[14653] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 5 5 5 5 5 1 5 5 5 5 5 5 5 5
[14689] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[14725] 3 3 3 3 3 3 3 3 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[14761] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 2 2 2
[14797] 1 2 2 2 2 2 2 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[14833] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 5 1
[14869] 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 3 3 3 3 3
[14905] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[14941] 3 3 3 3 3 3 3 1 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 2 2 2 4
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[21889] 5 5 5 5 5 5 5 5 5 5 3 3 3 5 3 3 3 3 3 3 3 3 3 3 3 1 3 2 2 2 2 2 2 2 2 2
[21925] 5 5 5 2 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[21961] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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[22033] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[22069] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[22105] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 5 5 5 5 5 5 5 5 2 2 2 2 2 1 1 1
[22141] 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 3 3 5 5 5 3 3 3 3 3 3
[22177] 3 3 3 2 2 2 2 2 2 2 2 1 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[22213] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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[22393] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[22429] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[22465] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 5 5 5 5 5 2 2 4 1 1 1 1 1 1 2 2 5
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[23077] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 2 2 1 1 1 1 1 1 1 1 2 2 2 2 2 1 1 2 2 2
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[23437] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2
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[23509] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[23545] 5 5 5 5 5 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 1 1 1
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[23617] 3 3 1 3 3 3 3 1 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 2 2 2 2
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[23725] 5 5 5 1 2 2 2 5 3 3 3 3 3 5 5 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3
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[24337] 5 5 1 1 1 1 1 1 2 1 1 1 1 2 2 2 2 2 2 1 5 3 5 5 5 3 5 5 1 5 5 5 5 5 5 3
[24373] 5 3 5 3 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3
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[24481] 3 3 3 1 3 3 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
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[24985] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
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[25057] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 2 1 1 1 2 2 3 2 2 2 2 5 5 5 5 5 5 5 5 5
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[25129] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[25165] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[25201] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
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[25345] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
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[25597] 1 1 1 1 1 2 2 2 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
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[25777] 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 5 5 1 5 5 5 1 5 5 1 5 5 5 5 5 5
[25813] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 5 5 5 5 5 1 1 5 5 5
[25849] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 1 3 3 3 3
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[25957] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
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[26029] 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3
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[26101] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[26137] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
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[26389] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1
[26425] 1 1 1 1 2 2 1 2 2 2 1 1 1 1 1 2 1 1 2 2 2 2 2 2 2 2 1 4 1 1 1 2 2 2 2 1
[26461] 2 5 5 1 3 5 5 5 5 5 5 5 1 5 5 3 3 3 5 5 5 5 5 5 5 3 3 3 3 5 1 5 5 3 5 5
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[26533] 5 3 3 5 5 5 3 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 1 1 3 1 3 3 3 3 1
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[26749] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4
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[27073] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3
[27109] 3 3 3 1 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 1 3 3 3 3 3
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[27217] 3 3 3 1 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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[27289] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[27325] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 1 1 1
[27361] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 1 1 1 1 5 2 2 5 2 5
[27397] 5 5 5 5 1 3 3 5 3 5 5 5 5 5 3 5 5 5 3 5 5 3 5 5 3 3 3 1 3 3 3 3 3 3 3 3
[27433] 3 1 3 3 3 3 3 1 1 3 3 3 3 1 3 3 3 3 3 1 3 1 3 3 1 3 3 3 2 2 2 2 2 5 2 2
[27469] 1 4 2 2 2 1 4 2 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 1 5 5 5 5
[27505] 1 1 5 5 5 5 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 5 5 5 5 5 5 5 5 5 5 1 5
[27541] 5 5 5 5 5 5 5 5 5 1 1 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 3
[27577] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3
[27613] 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 2
[27649] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2 4 2 2
[27685] 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[27721] 5 5 5 5 1 1 1 1 1 1 1 5 5 5 5 1 1 1 1 1 1 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[27757] 5 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[27793] 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[27829] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2
[27865] 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 3 3 3 3 3 1 3 3
[27901] 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 2 2 2 2 2 2 2 2 2 2 2 2
[27937] 2 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[27973] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5
[28009] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 5 1 1 1 2 2 1 1 1 2
[28045] 2 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[28081] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3
[28117] 3 2 2 2 2 2 1 1 2 5 5 3 3 3 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[28153] 3 3 1 2 2 2 2 2 2 2 2 2 5 2 1 2 2 2 1 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5
[28189] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1
[28225] 1 1 1 5 5 5 5 1 1 1 1 1 1 1 1 1 2 1 2 1 1 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5
[28261] 5 5 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 1
[28297] 5 5 1 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[28333] 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[28369] 3 3 3 3 3 3 3 3 3 3 3 3 3 5 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[28405] 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[28441] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5
[28477] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 2 1 2 2 2 5 2 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2
[28513] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[28549] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[28585] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[28621] 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 2
[28657] 2 2 2 2 5 5 5 5 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[28693] 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5
[28729] 5 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2
[28765] 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
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[28837] 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
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[56557] 5 5 5 5 3 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[56593] 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 3 3 3 3 3 3 3 3 3 3
[56629] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 5 5 5 5 5 5
[56665] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 5 5 5 5 5 5
[56701] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 2 1 1 5 5 5 1
[56737] 1 5 3 5 5 3 3 1 5 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 5 5 5 3
[56773] 5 5 5 5 5 3 3 3 3 3 5 5 2 5 5 5 5 5 5 1 5 3 5 5 5 5 1 5 5 5 5 5 5 5 5 5
[56809] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[56845] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[56881] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5
[56917] 5 3 3 3 3 3 3 3 5 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 2 5 5
[56953] 5 5 2 2 1 1 1 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 5 5 5 5 5 5
[56989] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 1 1 1 1 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3
[57025] 3 3 3 2 2 2 2 5 2 2 1 1 1 1 1 5 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[57061] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[57097] 5 5 5 3 3 3 3 2 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 2 5
[57133] 5 5 5 5 5 5 5 4 5 3 3 3 5 3 3 3 3 5 3 3 3 3 3 3 3 3 5 5 3 3 3 3 3 3 5 5
[57169] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 1 3 3 3 3 5 5 5 5 5 5 1 1 5 3 5 5 5
[57205] 5 5 3 5 3 3 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[57241] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 5 5 5 5 5 5 5 5 5 5 5 5
[57277] 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[57313] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[57349] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 2 1 1 5
[57385] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[57421] 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 5 5 5 5 5 5 5 5 5 5 1 4 1 4 1 1 5 5 5 5 3
[57457] 5 5 3 3 5 5 5 5 5 5 5 5 1 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[57493] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5
[57529] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[57565] 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1
[57601] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3
[57637] 3 3 2 2 2 5 5 5 5 5 5 5 5 2 2 2 2 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 5 5
[57673] 1 1 5 5 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[57709] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[57745] 5 5 5 5 5 5 5 5 5 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 5 5 5 3
[57781] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[57817] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5
[57853] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[57889] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5
[57925] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 1 3 3 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[57961] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 5 5 5 5 5 5 5
[57997] 5 5 5 5 5 5 5 5 5 5 5 5 1 2 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[58033] 5 5 5 3 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[58069] 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1
Within cluster sum of squares by cluster:
[1] 14693.4361 4768.9833 11563.6689 801.5362 4186.2120
(between_SS / total_SS = 79.3 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault"
dt$building_clusters = building_clusters$cluster
dt_c = copy(dt)
dt_c$borough = as.factor(dt_c$borough)
options(repr.plot.width = 10, repr.plot.height = 10, repr.plot.res = 200) # for graph sizes
ggplot(data = dt_c[,.(saleprice = mean(saleprice_wo, na.rm = TRUE)),.(saledate,borough)]
, aes(x = saledate, y = saleprice, group = borough, color = borough)) +
geom_line() +
facet_wrap(~borough, ncol = 1,scales = "free")
## without outliers
options(repr.plot.width = 10, repr.plot.height = 15, repr.plot.res = 200) # for graph sizes
ggplot(data = dt_c[,.(saleprice = mean(saleprice_wo, na.rm = TRUE)),.(saledate,b_class_group)]
, aes(x = saledate, y = saleprice, group = b_class_group, color = b_class_group)) +
geom_line() +
facet_wrap(~b_class_group, ncol = 1,scales = "free")
dt[, saleprice_wo := tend_outliers_keep(saleprice, sigma = 3),.(borough)]
dt[, saleprice_wo_3s := tend_outliers_keep(saleprice, sigma = 3),.(borough,b_class_group)]
dt[, saleprice_wo_IQR := tend_outliers_keep_IQR(saleprice, IQR = 1.5),.(borough,b_class_group)]
dt[, saleprice_log_wo_IQR := tend_outliers_keep_IQR(saleprice_log, IQR = 1.5),.(borough,b_class_group)]
dt[, saleprice_log_wo_3s := tend_outliers_keep(saleprice_log, sigma = 3),.(borough,b_class_group)]
summary( dt[,.(saleprice,saleprice_wo,saleprice_wo_3s,saleprice_wo_IQR
,saleprice_log_wo= exp(saleprice_log_wo)
,saleprice_log_wo_IQR = exp(saleprice_log_wo_IQR)
,saleprice_log_wo_3s = exp(saleprice_log_wo_3s))])
saleprice saleprice_wo saleprice_wo_3s saleprice_wo_IQR Min. : 210 Min. : 210 Min. : 210 Min. : 210 1st Qu.: 380000 1st Qu.: 380000 1st Qu.: 380000 1st Qu.: 380000 Median : 635000 Median : 635000 Median : 635000 Median : 630000 Mean : 1505757 Mean : 1127934 Mean : 1205344 Mean : 956561 3rd Qu.: 1080000 3rd Qu.: 1080000 3rd Qu.: 1073460 3rd Qu.: 1050000 Max. :2210000000 Max. :16971406 Max. :148538545 Max. :27750000 saleprice_log_wo saleprice_log_wo_IQR saleprice_log_wo_3s Min. : 26659 Min. : 210 Min. : 5997 1st Qu.: 380000 1st Qu.: 380000 1st Qu.: 380000 Median : 635000 Median : 635000 Median : 635000 Mean : 1194498 Mean : 1505757 Mean : 1367657 3rd Qu.: 1080000 3rd Qu.: 1080000 3rd Qu.: 1078932 Max. :27382670 Max. :2210000000 Max. :2210000000
dt_molten = dt[,.(borough,saleprice,saleprice_wo,saleprice_wo_3s,saleprice_wo_IQR
,saleprice_log_wo
,saleprice_log_wo_IQR
,saleprice_log_wo_3s)] %>%
melt.data.table(id.vars = c("borough"))
options(repr.plot.width = 10, repr.plot.height = 15, repr.plot.res = 200)
ggplot(data = dt_molten ,aes(x = value, fill = as.factor(borough)))+
facet_wrap(variable ~ borough, scales ="free", ncol = 5) +
geom_density() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
The nicest shape belongs to saleprice_log_wo :)
Any pattern in a year?
dt_outlier = dt[,.SD,.SDcols = c("saledate","b_class_group","saleprice_wo","saleprice_wo_3s","saleprice_wo_IQR")]
dt_outlier[, yearmonth := zoo::as.yearmon(saledate, "%Y%m")]
dt_outlier = dt_outlier[,.(saleprice_wo = mean(saleprice_wo),
saleprice_wo_3s = mean(saleprice_wo_3s),
saleprice_wo_IQR = mean(saleprice_wo_IQR)),
.(yearmonth,b_class_group)]
dt_molten = dt_outlier %>%
melt.data.table(id.vars = c("yearmonth","b_class_group"))
options(repr.plot.width = 10, repr.plot.height = 5, repr.plot.res = 200) # for graph sizes
ggplot(data = dt_molten
, aes(x = yearmonth, y = value, group = b_class_group, color = b_class_group)) +
geom_line() + facet_wrap(~variable,scales = "free", ncol = 1)
Any pattern explicable with week effect?
#dt[, wday := wday(saledate, label=TRUE)]
dt_outlier = dt[,.SD,.SDcols = c("saledate","b_class_group","saleprice_wo","saleprice_wo_3s","saleprice_wo_IQR")]
dt_outlier[, wday := wday(saledate, label=TRUE)]
dt_outlier = dt_outlier[,.(saleprice_wo = mean(saleprice_wo),
saleprice_wo_3s = mean(saleprice_wo_3s),
saleprice_wo_IQR = mean(saleprice_wo_IQR)),
.(wday,b_class_group)]
dt_molten = dt_outlier %>%
melt.data.table(id.vars = c("wday","b_class_group"))
options(repr.plot.width = 10, repr.plot.height = 5, repr.plot.res = 200) # for graph sizes
ggplot(data = dt_molten
, aes(x = wday, y = value, group = b_class_group, color = b_class_group)) +
geom_line() + facet_wrap(~variable,scales = "free", ncol = 1)
Totalunits = 0 and na square feet columns are very related.
table(dt$totalunits)
dt[,iszero := ifelse(totalunits == 0,"1","0")]
0 1 2 3 4 5 6 7 8 9 10 11 12
15943 26728 9976 2875 782 216 400 96 168 86 100 27 35
13 14 15 16 17 18 19 20 21 22 23 24 25
14 19 32 52 12 14 15 47 9 5 7 28 19
26 27 28 29 30 31 32 33 34 35 36 37 38
14 18 6 7 23 12 9 5 6 12 17 12 4
39 40 41 42 43 44 45 46 47 48 49 50 51
4 3 13 16 3 7 6 5 5 13 5 3 2
52 53 54 55 56 57 58 59 60 61 62 63 64
2 2 5 3 4 1 3 2 8 4 4 6 2
65 66 67 68 69 70 71 72 74 75 76 78 79
2 3 1 3 1 2 2 2 3 2 1 5 1
81 83 84 86 89 90 91 93 94 95 96 99 100
1 1 3 1 2 2 2 1 2 3 3 1 2
102 103 104 106 107 111 120 121 122 126 128 129 132
1 1 1 1 1 1 1 1 1 2 1 1 1
135 136 137 142 143 145 146 150 152 155 159 172 179
1 1 1 1 1 1 1 2 2 1 1 1 1
185 192 193 198 222 238 254 256 257 286 291 295 315
1 1 1 1 1 1 1 1 1 7 1 2 1
318 323 324 336 374 395 436 446 462 482 520 538 736
1 1 1 1 1 1 2 1 1 1 1 1 1
771 902 955 1866 2261
3 1 1 1 1
options(repr.plot.width = 5, repr.plot.height = 3, repr.plot.res = 200) # for graph sizes
ggplot(data = dt, aes(x = b_class_group, fill = as.factor(iszero), group = as.factor(iszero))) + geom_bar()
options(repr.plot.width = 5, repr.plot.height = 3, repr.plot.res = 200) # for graph sizes
ggplot(data = dt[b_class_group == "other"], aes(x = taxclassatpresent, fill = as.factor(iszero), group = as.factor(iszero))) + geom_bar()
Let's check if the sum of residentialunits and commercialunits gives us the total units. It seems that inconsistency is present mostly at tax class 4 ( which has the lowest tax rate https://www1.nyc.gov/site/finance/taxes/property-bills-and-payments.page). Moreover, most of these records has R_type building class which are like parking and storage spaces.
#dt[residentialunits + commercialunits != totalunits,.N,.(bor)]
#t(dt[idx == 200])
dt[(residentialunits + commercialunits != totalunits),.N,.(taxclass_present)]
dt[(residentialunits + commercialunits != totalunits),.N,.(buildingclassatpresent)][order(-N)] %>% head()
dt[(residentialunits + commercialunits != totalunits),.N,.(buildingclassatpresent)][order(-N)] %>% head() %>% select(N) %>% sum()
| taxclass_present | N |
|---|---|
| <dbl> | <int> |
| 4 | 757 |
| 1 | 11 |
| buildingclassatpresent | N |
|---|---|
| <chr> | <int> |
| rb | 241 |
| rg | 218 |
| rh | 81 |
| rk | 73 |
| rs | 66 |
| rp | 42 |
skim(dt[zipcode == 0]) #308 records
-- Data Summary ------------------------
Values
Name dt[zipcode == 0]
Number of rows 308
Number of columns 44
Key NULL
_______________________
Column type frequency:
character 10
factor 1
numeric 32
POSIXct 1
________________________
Group variables None
-- Variable type: character ----------------------------------------------------
# A tibble: 10 x 8
skim_variable n_missing complete_rate min max empty n_unique
* <chr> <int> <dbl> <int> <int> <int> <int>
1 neighborhood 32 0.896 6 23 0 102
2 buildingclasscategory 39 0.873 16 35 0 9
3 taxclassatpresent 0 1 1 2 0 4
4 buildingclassatpresent 0 1 2 2 0 19
5 address 0 1 5 6 0 3
6 buildingclassattimeofsale 0 1 2 2 0 19
7 bbl 0 1 6 12 0 300
8 b_class_present 0 1 1 1 0 10
9 b_class_group 0 1 1 5 0 4
10 iszero 0 1 1 1 0 2
whitespace
* <int>
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
-- Variable type: factor -------------------------------------------------------
# A tibble: 1 x 6
skim_variable n_missing complete_rate ordered n_unique
* <chr> <int> <dbl> <lgl> <int>
1 grosssquarefeet_log_group 0 1 FALSE 5
top_counts
* <chr>
1 [4.: 142, [8.: 101, [7.: 56, [8.: 5
-- Variable type: numeric ------------------------------------------------------
# A tibble: 32 x 11
skim_variable n_missing complete_rate mean sd
* <chr> <int> <dbl> <dbl> <dbl>
1 v1 0 1 8757. 7097.
2 borough 0 1 3.68 1.30
3 block 0 1 5303. 4227.
4 lot 0 1 350. 946.
5 zipcode 0 1 0 0
6 residentialunits 0 1 0.136 0.413
7 commercialunits 0 1 0.0227 0.220
8 totalunits 0 1 0.159 0.475
9 landsquarefeet 22 0.929 24956. 254757.
10 grosssquarefeet 299 0.0292 2681. 1634.
11 yearbuilt 0 1 1826. 66.8
12 taxclassattimeofsale 0 1 1.96 1.35
13 saleprice 0 1 1368123. 7339215.
14 idx 0 1 55528. 25487.
15 b_class_cat 39 0.873 12.5 11.7
16 taxclass_present 0 1 1.96 1.35
17 saleprice_log 0 1 12.4 2.01
18 saleprice_wo 0 1 716537. 1073433.
19 saleprice_wo_log 0 1 12.3 1.92
20 saleprice_log_wo 0 1 12.7 1.33
21 yearbuilt_group 0 1 1.26 0.675
22 totalunits_group 0 1 0.159 0.475
23 commercialunits_group 0 1 0.0195 0.179
24 grosssquarefeet_log_filled 0 1 7.57 0.972
25 commercialunits_sqrt 0 1 0.0167 0.150
26 residentialunits_sqrt 0 1 0.121 0.349
27 residentialunits_group 0 1 2.14 0.413
28 building_clusters 0 1 3.52 1.44
29 saleprice_wo_3s 0 1 918360. 1789064.
30 saleprice_wo_IQR 0 1 710318. 1038781.
31 saleprice_log_wo_IQR 0 1 12.4 2.01
32 saleprice_log_wo_3s 0 1 12.5 1.69
p0 p25 p50 p75 p100 hist
* <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 69 3608 6427 11646. 26431 <U+2587><U+2585><U+2583><U+2581><U+2582>
2 1 3 4 5 5 <U+2582><U+2583><U+2583><U+2586><U+2587>
3 16 1953 4320. 7596. 16180 <U+2587><U+2587><U+2583><U+2582><U+2582>
4 1 15.8 42 102. 3829 <U+2587><U+2581><U+2581><U+2581><U+2581>
5 0 0 0 0 0 <U+2581><U+2581><U+2587><U+2581><U+2581>
6 0 0 0 0 2 <U+2587><U+2581><U+2581><U+2581><U+2581>
7 0 0 0 0 3 <U+2587><U+2581><U+2581><U+2581><U+2581>
8 0 0 0 0 3 <U+2587><U+2581><U+2581><U+2581><U+2581>
9 180 2000 3513 5700 4252327 <U+2587><U+2581><U+2581><U+2581><U+2581>
10 300 2132 2327 2450 5625 <U+2582><U+2587><U+2582><U+2581><U+2583>
11 1800 1800 1800 1800 2016 <U+2587><U+2581><U+2581><U+2581><U+2581>
12 1 1 1 4 4 <U+2587><U+2581><U+2581><U+2581><U+2583>
13 315 90000 300000 796250 122000000 <U+2587><U+2581><U+2581><U+2581><U+2581>
14 5586 32819. 64844. 79378. 84168 <U+2583><U+2582><U+2582><U+2582><U+2587>
15 1 5 5 29 39 <U+2587><U+2581><U+2581><U+2583><U+2581>
16 1 1 1 4 4 <U+2587><U+2581><U+2581><U+2581><U+2583>
17 5.75 11.4 12.6 13.6 18.6 <U+2581><U+2582><U+2587><U+2583><U+2581>
18 315 90000 300000 796250 7500000 <U+2587><U+2581><U+2581><U+2581><U+2581>
19 5.75 11.4 12.6 13.6 15.8 <U+2581><U+2581><U+2583><U+2587><U+2583>
20 10.2 11.5 12.6 13.6 16.1 <U+2585><U+2587><U+2587><U+2583><U+2582>
21 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581>
22 0 0 0 0 3 <U+2587><U+2581><U+2581><U+2581><U+2581>
23 0 0 0 0 2 <U+2587><U+2581><U+2581><U+2581><U+2581>
24 5.70 6.69 8.00 8.43 10.1 <U+2583><U+2587><U+2585><U+2587><U+2581>
25 0 0 0 0 1.73 <U+2587><U+2581><U+2581><U+2581><U+2581>
26 0 0 0 0 1.41 <U+2587><U+2581><U+2581><U+2581><U+2581>
27 2 2 2 2 4 <U+2587><U+2581><U+2581><U+2581><U+2581>
28 1 2 3 5 5 <U+2581><U+2587><U+2582><U+2581><U+2587>
29 315 90000 300000 796250 16109903. <U+2587><U+2581><U+2581><U+2581><U+2581>
30 315 90000 300000 796250 7500000 <U+2587><U+2581><U+2581><U+2581><U+2581>
31 5.75 11.4 12.6 13.6 18.6 <U+2581><U+2582><U+2587><U+2583><U+2581>
32 9.00 11.4 12.6 13.6 17.4 <U+2583><U+2585><U+2587><U+2583><U+2581>
-- Variable type: POSIXct ------------------------------------------------------
# A tibble: 1 x 7
skim_variable n_missing complete_rate min max
* <chr> <int> <dbl> <dttm> <dttm>
1 saledate 0 1 2016-09-07 00:00:00 2017-08-23 00:00:00
median n_unique
* <dttm> <int>
1 2017-03-09 00:00:00 177
## deletion
dt = dt[zipcode != 0] # 308 rows
Most of NA landsquarefeet and grosssquarefeet happen when commercial units are equal to 0.
dt[,iszero := ifelse(is.na(grosssquarefeet),"1","0")]
ggplot(data = dt[totalunits < 10], aes(x = as.factor(totalunits), fill = as.factor(iszero), group = as.factor(iszero))) + geom_bar() + facet_wrap(~b_class_group)
dt[, saleprice_log := log(saleprice)]
draw_boxplot(dt,id_col = "taxclassatpresent", "saleprice_log")
# by using the info on https://www1.nyc.gov/site/finance/taxes/property-determining-your-assessed-value.page
dt[, assessment_ratio_present := ifelse(taxclass_present == 1,0.06,0.45)]
dt[,onlycommercial := ifelse(totalunits == commercialunits,1,0)]
# maybe new feature?
a = dt[,.N,.(onlycommercial,neighborhood)]
onlycommercial_neighborhoods = a[,.N,.(neighborhood)][N == 1]$neighborhood
nrow(dt[neighborhood %in% onlycommercial_neighborhoods])
#nah, nothing important
#maybe degree of commercial?
a[,sum_N := sum(N),.(neighborhood)]
a=a[onlycommercial == 1, .(ratio = N / sum_N,neighborhood)]
options(repr.plot.width =3, repr.plot.height = 3, repr.plot.res = 200) # for graph sizes
hist(a$ratio)
highlycommercial_neighborhoods = a[ratio > 0.4]$neighborhood
nrow(dt[neighborhood %in% highlycommercial_neighborhoods])
#cool
dt[,highly_commercial := ifelse(neighborhood %in% highlycommercial_neighborhoods,1,0)]
encode_cols = c("b_class_present","b_class_group","address","taxclassatpresent")
dt[,paste0((encode_cols),"_encoded"):= lapply(.SD, encode_numeric), .SDcols = encode_cols]
dt[,address_encoded := encode_numeric(address)]
dt[,b_class_group_encoded := encode_numeric(b_class_group)]
dt[,taxclassatpresent_encoded := encode_numeric(taxclassatpresent)]
dt[,b_class_present_encoded := encode_numeric(b_class_present)]
dt[,grosssquarefeet_log_group_encoded := encode_numeric(grosssquarefeet_log_group)]
dt[, commercialunits_log := log(commercialunits+0.01)]
dt[, residentialunits_log := log(residentialunits+0.01)]
dt[, commercialunits_wo := tend_outliers_keep_IQR(commercialunits, IQR = 1.5)]
dt[, commercialunits_log := log(commercialunits_wo+0.01)]
dt_molten = dt %>% purrr::keep(is.numeric) %>%
tidyr::gather()
options(repr.plot.width=12, repr.plot.height=15) # for graph sizes
ggplot(data = dt_molten,aes(x = value, fill = key))+
facet_wrap(~ key, scales = "free", ncol = 4) +
geom_density(fill = "green") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
Warning message: "Removed 85686 rows containing non-finite values (stat_density)."
Log + outlier
#feature_list = c("borough","residentialunits","commercialunits","address_encoded","b_class_present_encoded","taxclassatpresent_encoded")
feature_list = c("borough","residentialunits_group","commercialunits_group","address_encoded","b_class_present_encoded","taxclassatpresent_encoded","highly_commercial","building_clusters")
target = c("saleprice_log_wo","saleprice_log")
dt_model = copy(dt[,.SD,.SDcols = c("idx",target,feature_list)])
chunk_no = 10
set.seed(0)
folds <- cut(seq(1,nrow(dt_model)),breaks=chunk_no,labels=FALSE)
pred_table = data.table()
imp_table = data.table()
fitted_table = data.table()
for(i in 1:chunk_no){
#Segment your data by fold using the which() function
testIndexes <- which(folds==i,arr.ind=TRUE)
testData <- dt_model[testIndexes, ]
trainData <- dt_model[-testIndexes, ]
target = "saleprice_log_wo"
y_train = trainData[[target]]
target = "saleprice_log"
y_test = testData[[target]]
Scale_Parameters = get_scale_params(trainData, feature_list)
x_train = scale(trainData[,.SD,.SDcols = feature_list])
x_test = testData[,.SD,.SDcols = feature_list]
scale_external(x_test,Scale_Parameters)
ls.model <- cv.glmnet( x_train , y_train , type.measure="mse", alpha=1, family="gaussian", nfolds = 5)
lasso_imp = coef(ls.model)
lasso_imp = t(t(lasso_imp[order(lasso_imp),]))
lasso_imp = data.table("names" = names(lasso_imp[,1]), "coeff" = lasso_imp[,1])
if(str_detect(target,"log") == TRUE){
pred = exp(predict(ls.model, as.matrix(x_test), s = "lambda.min"))
actual = exp(y_test)
fitted = exp(predict(ls.model, as.matrix(x_train), s = "lambda.min"))
}else{
pred = predict(ls.model, as.matrix(x_test), s = "lambda.min")
actual = y_test
fitted = exp(predict(ls.model, as.matrix(x_train)))
}
#Check performance
sub_pred_table = testData[,.(idx, actual = actual, pred = pred, chunk = i)]
sub_fitted_table = trainData[,.(idx, fitted = fitted, chunk = i )]
pred_table = rbind(pred_table,sub_pred_table )
fitted_table = rbind(fitted_table,sub_fitted_table )
}
<sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient
Since some problem occured with caret package, unfortunately, I am not able to share the results.
#feature_list = c("borough","residentialunits","commercialunits","address_encoded","b_class_present_encoded","taxclassatpresent_encoded")
feature_list = c("borough","residentialunits_group","address_encoded","b_class_present_encoded","taxclassatpresent_encoded")
target = "saleprice_log"
dt_model = copy(dt[,.SD,.SDcols = c("idx",target,feature_list)])
chunk_no = 10
set.seed(0)
folds <- cut(seq(1,nrow(dt_model)),breaks=chunk_no,labels=FALSE)
pred_table = data.table()
imp_table = data.table()
fitted_table = data.table()
#for(i in 1:chunk_no){
# #Segment your data by fold using the which() function
#
# testIndexes <- which(folds==i,arr.ind=TRUE)
# testData <- dt_model[testIndexes, ]
# trainData <- dt_model[-testIndexes, ]
#
# y_train = trainData[[target]]
# y_test = testData[[target]]
#
# Scale_Parameters = get_scale_params(trainData, feature_list)
# x_train = scale(trainData[,.SD,.SDcols = feature_list])
#
# x_test = testData[,.SD,.SDcols = feature_list]
# scale_external(x_test,Scale_Parameters)
#
# for (k in c(5)){ #3,5,10,15
# knn = caret::knnregTrain(train = x_train, test = x_test, y = y_train, k = k)
# print(paste0("knn k= ",k," rmse: ",calc_rmse(y_test, knn)))
# }
#
# if(str_detect(target,"log") == TRUE){
# pred = exp(knn)
# actual = exp(y_test)
# }else{
# pred = knn
# actual = y_test
# }
#
# #Check performance
# sub_pred_table = testData[,.(idx, actual = actual, pred = pred, chunk = i)]
# sub_fitted_table = trainData[,.(idx, fitted = fitted, chunk = i )]
#
# pred_table = rbind(pred_table,sub_pred_table )
# fitted_table = rbind(fitted_table,sub_fitted_table )
#
#}
#
#feature_list = c("borough","residentialunits","commercialunits","address_encoded","b_class_present_encoded","taxclassatpresent_encoded")
feature_list = c("borough","residentialunits_group","address_encoded","b_class_present_encoded","taxclassatpresent_encoded")
target = "saleprice_log"
dt_model = copy(dt[,.SD,.SDcols = c("idx",target,feature_list)])
chunk_no = 10
set.seed(0)
folds <- cut(seq(1,nrow(dt_model)),breaks=chunk_no,labels=FALSE)
pred_table = data.table()
imp_table = data.table()
fitted_table = data.table()
6 min for the 1st chunk
for(i in 1:1){
#Segment your data by fold using the which() function
testIndexes <- which(folds==i,arr.ind=TRUE)
testData <- dt_model[testIndexes, ]
trainData <- dt_model[-testIndexes, ]
y_train = trainData[[target]]
y_test = testData[[target]]
Scale_Parameters = get_scale_params(trainData, feature_list)
x_train = scale(trainData[,.SD,.SDcols = feature_list])
x_test = testData[,.SD,.SDcols = feature_list]
scale_external(x_test,Scale_Parameters)
svm.model <- svm( x_train , y_train)
if(str_detect(target,"log") == TRUE){
pred = exp(predict(svm.model, as.matrix(x_test)))
actual = exp(y_test)
}else{
pred = predict(svm.model, as.matrix(x_test))
actual = y_test
fitted = exp(predict(svm.model, as.matrix(x_train)))
}
#Check performance
sub_pred_table = testData[,.(idx, actual = actual, pred = pred, chunk = i)]
sub_fitted_table = trainData[,.(idx, fitted = fitted, chunk = i )]
pred_table = rbind(pred_table,sub_pred_table )
fitted_table = rbind(fitted_table,sub_fitted_table )
}
## Results for only 1 chunk
print("overall test rmse:")
calc_rmse(pred_table$pred,pred_table$actual)
calc_rmse(pred_table[actual < 20000000]$pred,pred_table[actual < 20000000]$actual)
[1] "overall test rmse:"
A general model with everything vs different models for borough and building type.
params = list( booster = "gbtree"
# , eta = best_params$eta #learning rate
# , gamma = best_params$gamma # min loss reduction
# , max_depth = best_params$depth
, min_child_weight = 1, subsample = 1, colsample_bytree= 1
, objective = "reg:squarederror"
, eval_metric = "rmse")
#feature_list = c("borough","zipcode","residentialunits","commercialunits","address_encoded","b_class_present_encoded","taxclassatpresent_encoded")
feature_list = c("borough","b_class_group_encoded"
,"zipcode","commercialunits_group","residentialunits_group","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
)
target = "saleprice"
fit = model_xgboost(feature_list,target,chunk_no = 10)
pred_table = fit[[1]]
imp_table = fit[[2]]
imp_table
print("overall test rmse:")
calc_rmse(pred_table$pred,pred_table$actual)
calc_rmse(pred_table[actual < 20000000]$pred,pred_table[actual < 20000000]$actual)
[1] train-rmse:4244969.000000 test-rmse:36897200.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4026631.500000 test-rmse:36494472.000000 [3] train-rmse:3898269.250000 test-rmse:36266436.000000 [4] train-rmse:3757441.250000 test-rmse:36129340.000000 [5] train-rmse:3671084.750000 test-rmse:36042768.000000 [6] train-rmse:3617252.500000 test-rmse:35979220.000000 [7] train-rmse:3563825.750000 test-rmse:35942720.000000 [8] train-rmse:3511676.500000 test-rmse:35891592.000000 [9] train-rmse:3465778.000000 test-rmse:35891844.000000 [10] train-rmse:3396834.500000 test-rmse:35873512.000000 [11] train-rmse:3382777.500000 test-rmse:35862776.000000 [12] train-rmse:3362368.500000 test-rmse:35861236.000000 [13] train-rmse:3344753.750000 test-rmse:35837356.000000 [14] train-rmse:3328556.750000 test-rmse:35838036.000000 [15] train-rmse:3320809.250000 test-rmse:35829864.000000 [16] train-rmse:3301355.250000 test-rmse:35829880.000000 [17] train-rmse:3291317.500000 test-rmse:35851656.000000 [18] train-rmse:3282484.750000 test-rmse:35851640.000000 Stopping. Best iteration: [15] train-rmse:3320809.250000 test-rmse:35829864.000000 [1] train-rmse:11206957.000000 test-rmse:7006730.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9780952.000000 test-rmse:7017774.000000 [3] train-rmse:8674032.000000 test-rmse:7049538.000000 [4] train-rmse:7750119.500000 test-rmse:7047136.000000 Stopping. Best iteration: [1] train-rmse:11206957.000000 test-rmse:7006730.500000 [1] train-rmse:12110462.000000 test-rmse:4807432.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:10496632.000000 test-rmse:4639436.500000 [3] train-rmse:9270359.000000 test-rmse:4530210.000000 [4] train-rmse:8296373.500000 test-rmse:4556145.000000 [5] train-rmse:7543714.500000 test-rmse:4554027.000000 [6] train-rmse:6934859.000000 test-rmse:4470436.000000 [7] train-rmse:6430612.000000 test-rmse:4434194.000000 [8] train-rmse:6052018.000000 test-rmse:4379107.500000 [9] train-rmse:5967806.000000 test-rmse:4376177.000000 [10] train-rmse:5673473.500000 test-rmse:4344745.000000 [11] train-rmse:5409859.500000 test-rmse:4363316.000000 [12] train-rmse:5232927.500000 test-rmse:4362422.500000 [13] train-rmse:5188676.000000 test-rmse:4369956.500000 Stopping. Best iteration: [10] train-rmse:5673473.500000 test-rmse:4344745.000000 [1] train-rmse:11320115.000000 test-rmse:2684714.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9920796.000000 test-rmse:2613562.250000 [3] train-rmse:8838465.000000 test-rmse:2600564.000000 [4] train-rmse:7953702.000000 test-rmse:2450345.500000 [5] train-rmse:7295497.000000 test-rmse:2440137.000000 [6] train-rmse:6759859.000000 test-rmse:2522722.500000 [7] train-rmse:6333528.500000 test-rmse:2512339.500000 [8] train-rmse:6145106.000000 test-rmse:2510635.500000 Stopping. Best iteration: [5] train-rmse:7295497.000000 test-rmse:2440137.000000 [1] train-rmse:11160879.000000 test-rmse:6732558.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9756083.000000 test-rmse:6648579.500000 [3] train-rmse:8665321.000000 test-rmse:6594260.500000 [4] train-rmse:7784674.000000 test-rmse:6571700.500000 [5] train-rmse:7116519.000000 test-rmse:6558787.500000 [6] train-rmse:6587409.000000 test-rmse:6549358.500000 [7] train-rmse:6149335.500000 test-rmse:6543710.500000 [8] train-rmse:5956323.000000 test-rmse:6536823.000000 [9] train-rmse:5634607.000000 test-rmse:6531974.500000 [10] train-rmse:5410717.000000 test-rmse:6531482.000000 [11] train-rmse:5217774.000000 test-rmse:6530127.000000 [12] train-rmse:5173503.500000 test-rmse:6528234.000000 [13] train-rmse:5099546.000000 test-rmse:6525673.500000 [14] train-rmse:4979397.500000 test-rmse:6525494.000000 [15] train-rmse:4943755.000000 test-rmse:6517729.500000 [16] train-rmse:4912540.500000 test-rmse:6519015.500000 [17] train-rmse:4820387.000000 test-rmse:6519179.000000 [18] train-rmse:4739226.000000 test-rmse:6517837.000000 Stopping. Best iteration: [15] train-rmse:4943755.000000 test-rmse:6517729.500000 [1] train-rmse:11295301.000000 test-rmse:3646133.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9897799.000000 test-rmse:3539547.000000 [3] train-rmse:8814405.000000 test-rmse:3445770.250000 [4] train-rmse:7933092.000000 test-rmse:3419395.250000 [5] train-rmse:7271624.500000 test-rmse:3370367.750000 [6] train-rmse:6755070.000000 test-rmse:3362571.000000 [7] train-rmse:6329191.000000 test-rmse:3346511.000000 [8] train-rmse:6135846.500000 test-rmse:3334735.750000 [9] train-rmse:5773442.000000 test-rmse:3330970.250000 [10] train-rmse:5489211.500000 test-rmse:3329825.000000 [11] train-rmse:5282525.000000 test-rmse:3332218.500000 [12] train-rmse:5132956.500000 test-rmse:3331079.000000 [13] train-rmse:5029644.500000 test-rmse:3334832.750000 Stopping. Best iteration: [10] train-rmse:5489211.500000 test-rmse:3329825.000000 [1] train-rmse:11327814.000000 test-rmse:2438194.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9927146.000000 test-rmse:2387625.250000 [3] train-rmse:8833415.000000 test-rmse:2386143.750000 [4] train-rmse:7959420.000000 test-rmse:2462976.000000 [5] train-rmse:7299684.500000 test-rmse:2457541.000000 [6] train-rmse:6776162.500000 test-rmse:2455054.000000 Stopping. Best iteration: [3] train-rmse:8833415.000000 test-rmse:2386143.750000 [1] train-rmse:11270161.000000 test-rmse:4660608.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9850548.000000 test-rmse:4633300.500000 [3] train-rmse:8754239.000000 test-rmse:4642232.000000 [4] train-rmse:7854909.000000 test-rmse:4703315.500000 [5] train-rmse:7189281.500000 test-rmse:4711098.500000 Stopping. Best iteration: [2] train-rmse:9850548.000000 test-rmse:4633300.500000 [1] train-rmse:11335440.000000 test-rmse:2117390.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9935399.000000 test-rmse:2081912.125000 [3] train-rmse:8854537.000000 test-rmse:2072334.250000 [4] train-rmse:7973753.500000 test-rmse:2064243.875000 [5] train-rmse:7314520.000000 test-rmse:2077700.000000 [6] train-rmse:6783986.500000 test-rmse:2077741.375000 [7] train-rmse:6359411.000000 test-rmse:2079397.500000 Stopping. Best iteration: [4] train-rmse:7973753.500000 test-rmse:2064243.875000 [1] train-rmse:11349714.000000 test-rmse:1187376.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9949856.000000 test-rmse:16886688.000000 [3] train-rmse:8856874.000000 test-rmse:30958958.000000 [4] train-rmse:7984381.500000 test-rmse:31178884.000000 Stopping. Best iteration: [1] train-rmse:11349714.000000 test-rmse:1187376.875000
| Feature | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| zipcode | 0.39253360 | 0.8164719688 | 0.758510203 | 0.7980900070 | 0.791233138 | 0.804270650 | 0.8018064029 | 0.805992240 | 0.8057535788 | 0.548230730 |
| building_clusters | 0.04162015 | 0.0998416757 | 0.101532361 | 0.1054774692 | 0.099389054 | 0.100985898 | 0.1063890565 | 0.104056023 | 0.1034287252 | 0.098318354 |
| commercialunits_group | 0.10946186 | 0.0364813836 | 0.031959159 | 0.0365645206 | 0.036639116 | 0.033059983 | 0.0360641903 | 0.036024378 | 0.0349764508 | 0.038597810 |
| address_encoded | 0.06201821 | 0.0141769572 | 0.032244109 | 0.0203960599 | 0.024917270 | 0.021550293 | 0.0177112827 | 0.017726009 | 0.0179667260 | 0.017164530 |
| b_class_group_encoded | 0.09706340 | 0.0127703209 | 0.010503758 | 0.0124074965 | 0.018297213 | 0.009175303 | 0.0073557569 | 0.006615443 | 0.0081750049 | 0.004974304 |
| highly_commercial | 0.03234904 | 0.0040426918 | 0.010788299 | 0.0097260623 | 0.010426498 | 0.009787730 | 0.0099422887 | 0.010399352 | 0.0096239268 | 0.010206110 |
| taxclass_present | 0.06715878 | 0.0085910240 | 0.008752791 | 0.0088537046 | 0.008326027 | 0.008297487 | 0.0095082066 | 0.008624524 | 0.0089758494 | 0.009328126 |
| residentialunits_group | 0.07494638 | 0.0052251738 | 0.005159020 | 0.0053910554 | 0.005550276 | 0.005007051 | 0.0046283425 | 0.004685668 | 0.0044592682 | 0.005763518 |
| onlycommercial | 0.07704466 | 0.0019810168 | 0.014115998 | 0.0028086942 | 0.002931058 | 0.003761594 | 0.0060760983 | 0.005693697 | 0.0063375083 | 0.004170385 |
| borough | 0.04580392 | 0.0004177874 | 0.026434301 | 0.0002849302 | 0.002290350 | 0.004104013 | 0.0005183746 | 0.000182666 | 0.0003029617 | 0.263246134 |
[1] "overall test rmse:"
print("please compare with knn:")
calc_rmse(pred_table[chunk ==1]$pred,pred_table[chunk ==1]$actual)
[1] "please compare with knn:"
#feature_list = c("borough","zipcode","residentialunits","commercialunits","address_encoded","b_class_present_encoded","taxclassatpresent_encoded")
feature_list = c("borough","b_class_group_encoded"
,"zipcode","commercialunits_group","residentialunits_group","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
)
target = "saleprice_log"
fit = model_xgboost(feature_list,target,chunk_no = 10)
pred_table = fit[[1]]
imp_table = fit[[2]]
imp_table
print("overall test rmse:")
calc_rmse(pred_table$pred,pred_table$actual)
calc_rmse(pred_table[actual < 20000000]$pred,pred_table[actual < 20000000]$actual)
[1] train-rmse:9.015181 test-rmse:9.605144 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.340274 test-rmse:6.724190 [3] train-rmse:4.478600 test-rmse:4.654668 [4] train-rmse:3.189958 test-rmse:3.229381 [5] train-rmse:2.307349 test-rmse:2.309324 [6] train-rmse:1.713515 test-rmse:1.696084 [7] train-rmse:1.322782 test-rmse:1.375553 [8] train-rmse:1.078677 test-rmse:1.192670 [9] train-rmse:0.935439 test-rmse:1.097197 [10] train-rmse:0.852653 test-rmse:1.063898 [11] train-rmse:0.805861 test-rmse:1.050811 [12] train-rmse:0.782749 test-rmse:1.054734 [13] train-rmse:0.765518 test-rmse:1.060228 [14] train-rmse:0.757321 test-rmse:1.066706 Stopping. Best iteration: [11] train-rmse:0.805861 test-rmse:1.050811 [1] train-rmse:9.028004 test-rmse:9.582934 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.349119 test-rmse:6.801970 [3] train-rmse:4.484442 test-rmse:4.867545 [4] train-rmse:3.193125 test-rmse:3.553299 [5] train-rmse:2.309895 test-rmse:2.684456 [6] train-rmse:1.715454 test-rmse:2.100116 [7] train-rmse:1.325246 test-rmse:1.665738 [8] train-rmse:1.082143 test-rmse:1.429912 [9] train-rmse:0.935025 test-rmse:1.297482 [10] train-rmse:0.851401 test-rmse:1.222321 [11] train-rmse:0.805505 test-rmse:1.178678 [12] train-rmse:0.780156 test-rmse:1.155750 [13] train-rmse:0.765228 test-rmse:1.142510 [14] train-rmse:0.756406 test-rmse:1.131588 [15] train-rmse:0.750344 test-rmse:1.128784 [16] train-rmse:0.746558 test-rmse:1.131164 [17] train-rmse:0.743334 test-rmse:1.129107 [18] train-rmse:0.741283 test-rmse:1.127654 [19] train-rmse:0.739200 test-rmse:1.122919 [20] train-rmse:0.736893 test-rmse:1.113966 [1] train-rmse:9.084031 test-rmse:9.044915 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.388622 test-rmse:6.349559 [3] train-rmse:4.511964 test-rmse:4.480175 [4] train-rmse:3.212098 test-rmse:3.209702 [5] train-rmse:2.320979 test-rmse:2.342276 [6] train-rmse:1.721623 test-rmse:1.782913 [7] train-rmse:1.327065 test-rmse:1.414214 [8] train-rmse:1.081810 test-rmse:1.189393 [9] train-rmse:0.935315 test-rmse:1.072078 [10] train-rmse:0.851641 test-rmse:1.004050 [11] train-rmse:0.806558 test-rmse:0.977608 [12] train-rmse:0.781707 test-rmse:0.960935 [13] train-rmse:0.769306 test-rmse:0.955103 [14] train-rmse:0.760333 test-rmse:0.954141 [15] train-rmse:0.753508 test-rmse:0.948668 [16] train-rmse:0.749242 test-rmse:0.948878 [17] train-rmse:0.745730 test-rmse:0.949385 [18] train-rmse:0.742452 test-rmse:0.948895 Stopping. Best iteration: [15] train-rmse:0.753508 test-rmse:0.948668 [1] train-rmse:9.082789 test-rmse:9.068410 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.387727 test-rmse:6.401456 [3] train-rmse:4.510948 test-rmse:4.541082 [4] train-rmse:3.211762 test-rmse:3.250616 [5] train-rmse:2.321101 test-rmse:2.370115 [6] train-rmse:1.722780 test-rmse:1.779946 [7] train-rmse:1.332564 test-rmse:1.408458 [8] train-rmse:1.088916 test-rmse:1.183477 [9] train-rmse:0.941815 test-rmse:1.049363 [10] train-rmse:0.859794 test-rmse:0.980006 [11] train-rmse:0.814075 test-rmse:0.937677 [12] train-rmse:0.789742 test-rmse:0.916930 [13] train-rmse:0.773348 test-rmse:0.905626 [14] train-rmse:0.763230 test-rmse:0.898787 [15] train-rmse:0.757550 test-rmse:0.896984 [16] train-rmse:0.754188 test-rmse:0.895310 [17] train-rmse:0.750936 test-rmse:0.890893 [18] train-rmse:0.746636 test-rmse:0.881518 [19] train-rmse:0.744455 test-rmse:0.881883 [20] train-rmse:0.742025 test-rmse:0.880423 [1] train-rmse:9.081030 test-rmse:9.022454 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.385472 test-rmse:6.298778 [3] train-rmse:4.508181 test-rmse:4.446748 [4] train-rmse:3.207537 test-rmse:3.151561 [5] train-rmse:2.315926 test-rmse:2.294023 [6] train-rmse:1.714230 test-rmse:1.695072 [7] train-rmse:1.321357 test-rmse:1.354367 [8] train-rmse:1.075918 test-rmse:1.171551 [9] train-rmse:0.928252 test-rmse:1.075351 [10] train-rmse:0.846097 test-rmse:1.042669 [11] train-rmse:0.799101 test-rmse:1.030665 [12] train-rmse:0.772061 test-rmse:1.025120 [13] train-rmse:0.757978 test-rmse:1.027663 [14] train-rmse:0.749308 test-rmse:1.031680 [15] train-rmse:0.743628 test-rmse:1.036040 Stopping. Best iteration: [12] train-rmse:0.772061 test-rmse:1.025120 [1] train-rmse:9.071951 test-rmse:9.197525 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.381032 test-rmse:6.520367 [3] train-rmse:4.507236 test-rmse:4.643519 [4] train-rmse:3.210244 test-rmse:3.349976 [5] train-rmse:2.321700 test-rmse:2.478665 [6] train-rmse:1.725911 test-rmse:1.892963 [7] train-rmse:1.337016 test-rmse:1.506742 [8] train-rmse:1.092137 test-rmse:1.231973 [9] train-rmse:0.947872 test-rmse:1.069711 [10] train-rmse:0.867655 test-rmse:0.977220 [11] train-rmse:0.819862 test-rmse:0.930572 [12] train-rmse:0.794137 test-rmse:0.893054 [13] train-rmse:0.781116 test-rmse:0.873717 [14] train-rmse:0.772877 test-rmse:0.861312 [15] train-rmse:0.768726 test-rmse:0.853807 [16] train-rmse:0.764194 test-rmse:0.847560 [17] train-rmse:0.759232 test-rmse:0.836168 [18] train-rmse:0.755679 test-rmse:0.834578 [19] train-rmse:0.754050 test-rmse:0.832461 [20] train-rmse:0.751817 test-rmse:0.831874 [1] train-rmse:9.094761 test-rmse:8.954057 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.398485 test-rmse:6.299432 [3] train-rmse:4.521297 test-rmse:4.458071 [4] train-rmse:3.222202 test-rmse:3.194034 [5] train-rmse:2.332237 test-rmse:2.350772 [6] train-rmse:1.736653 test-rmse:1.755493 [7] train-rmse:1.345650 test-rmse:1.391703 [8] train-rmse:1.103771 test-rmse:1.142920 [9] train-rmse:0.960563 test-rmse:0.991496 [10] train-rmse:0.880642 test-rmse:0.900410 [11] train-rmse:0.834017 test-rmse:0.857215 [12] train-rmse:0.810628 test-rmse:0.823725 [13] train-rmse:0.796716 test-rmse:0.803811 [14] train-rmse:0.789318 test-rmse:0.802393 [15] train-rmse:0.782302 test-rmse:0.795709 [16] train-rmse:0.775928 test-rmse:0.783663 [17] train-rmse:0.771639 test-rmse:0.785574 [18] train-rmse:0.768212 test-rmse:0.907674 [19] train-rmse:0.765204 test-rmse:0.904933 Stopping. Best iteration: [16] train-rmse:0.775928 test-rmse:0.783663 [1] train-rmse:9.112952 test-rmse:8.776073 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.411417 test-rmse:6.195089 [3] train-rmse:4.530859 test-rmse:4.376379 [4] train-rmse:3.228781 test-rmse:3.122079 [5] train-rmse:2.338508 test-rmse:2.245964 [6] train-rmse:1.742275 test-rmse:1.649692 [7] train-rmse:1.353366 test-rmse:1.258968 [8] train-rmse:1.108554 test-rmse:1.008983 [9] train-rmse:0.962738 test-rmse:0.856198 [10] train-rmse:0.880168 test-rmse:0.764635 [11] train-rmse:0.834707 test-rmse:0.711219 [12] train-rmse:0.811010 test-rmse:0.680995 [13] train-rmse:0.797797 test-rmse:0.664099 [14] train-rmse:0.789839 test-rmse:0.657619 [15] train-rmse:0.783109 test-rmse:0.653602 [16] train-rmse:0.777572 test-rmse:0.649450 [17] train-rmse:0.774618 test-rmse:0.650614 [18] train-rmse:0.772051 test-rmse:0.648267 [19] train-rmse:0.769650 test-rmse:0.646405 [20] train-rmse:0.767746 test-rmse:0.644789 [1] train-rmse:9.117465 test-rmse:8.704325 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.413636 test-rmse:6.021122 [3] train-rmse:4.531519 test-rmse:4.205935 [4] train-rmse:3.228414 test-rmse:2.917832 [5] train-rmse:2.336364 test-rmse:2.052964 [6] train-rmse:1.738378 test-rmse:1.476132 [7] train-rmse:1.347053 test-rmse:1.111103 [8] train-rmse:1.101035 test-rmse:0.890053 [9] train-rmse:0.957444 test-rmse:0.774884 [10] train-rmse:0.876524 test-rmse:0.725744 [11] train-rmse:0.831383 test-rmse:0.710918 [12] train-rmse:0.804240 test-rmse:0.708195 [13] train-rmse:0.788467 test-rmse:0.708780 [14] train-rmse:0.779283 test-rmse:0.712996 [15] train-rmse:0.772823 test-rmse:0.714178 Stopping. Best iteration: [12] train-rmse:0.804240 test-rmse:0.708195 [1] train-rmse:9.115464 test-rmse:8.796742 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.412511 test-rmse:6.238903 [3] train-rmse:4.530675 test-rmse:4.042737 [4] train-rmse:3.227801 test-rmse:2.828327 [5] train-rmse:2.335720 test-rmse:1.660402 [6] train-rmse:1.737158 test-rmse:1.125256 [7] train-rmse:1.346314 test-rmse:0.841753 [8] train-rmse:1.099704 test-rmse:0.746607 [9] train-rmse:0.955374 test-rmse:0.759056 [10] train-rmse:0.873465 test-rmse:0.798603 [11] train-rmse:0.826786 test-rmse:0.822289 Stopping. Best iteration: [8] train-rmse:1.099704 test-rmse:0.746607
| Feature | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| zipcode | 0.34741699 | 0.369159131 | 0.41937417 | 0.39755637 | 0.37630405 | 0.39433256 | 0.39224332 | 0.39185131 | 0.38315148 | 0.377285805 |
| borough | 0.14117768 | 0.125191336 | 0.14477561 | 0.16331159 | 0.17457872 | 0.17377497 | 0.16658327 | 0.15965033 | 0.14943973 | 0.144112945 |
| taxclass_present | 0.14663807 | 0.116926111 | 0.12464467 | 0.10812566 | 0.12045090 | 0.09065736 | 0.05888988 | 0.07167369 | 0.10901944 | 0.111173136 |
| building_clusters | 0.03869330 | 0.073610924 | 0.08260858 | 0.09122481 | 0.09264541 | 0.09450354 | 0.09388947 | 0.09441654 | 0.10260943 | 0.095791093 |
| onlycommercial | 0.15727368 | 0.106919101 | 0.07996558 | 0.07992588 | 0.08418488 | 0.08287896 | 0.06872065 | 0.05869818 | 0.09151990 | 0.094652655 |
| residentialunits_group | 0.03077724 | 0.048039418 | 0.04128153 | 0.05983227 | 0.04307243 | 0.02936097 | 0.05254068 | 0.05717811 | 0.03486787 | 0.041560820 |
| b_class_group_encoded | 0.05006407 | 0.068585779 | 0.04801472 | 0.04043573 | 0.04138632 | 0.05398890 | 0.07816688 | 0.07853008 | 0.04857100 | 0.046759459 |
| commercialunits_group | 0.05599813 | 0.063117294 | 0.03090834 | 0.03317610 | 0.03426678 | 0.05029455 | 0.05997776 | 0.05645977 | 0.04951313 | 0.064114090 |
| highly_commercial | 0.02103257 | 0.018584176 | 0.01537385 | 0.01274842 | 0.01768986 | 0.01599048 | 0.01516145 | 0.01953154 | 0.01894645 | 0.015982044 |
| address_encoded | 0.01092828 | 0.009866729 | 0.01305296 | 0.01366317 | 0.01542065 | 0.01421771 | 0.01382665 | 0.01201044 | 0.01236157 | 0.008567953 |
[1] "overall test rmse:"
## units groups vs log versions
feature_list = c("borough","b_class_group_encoded"
,"zipcode","commercialunits_log","residentialunits_log","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
)
target = "saleprice_log"
fit = model_xgboost(feature_list,target,chunk_no = 10)
pred_table = fit[[1]]
imp_table = fit[[2]]
imp_table
## target = saleprice
print("overall test rmse:")
calc_rmse(pred_table$pred,pred_table$actual)
calc_rmse(pred_table[actual < 20000000]$pred,pred_table[actual < 20000000]$actual)
[1] train-rmse:9.014654 test-rmse:9.600600 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.339021 test-rmse:6.753989 [3] train-rmse:4.476543 test-rmse:4.681537 [4] train-rmse:3.186913 test-rmse:3.262213 [5] train-rmse:2.303090 test-rmse:2.322323 [6] train-rmse:1.707287 test-rmse:1.719528 [7] train-rmse:1.318257 test-rmse:1.372497 [8] train-rmse:1.072905 test-rmse:1.164161 [9] train-rmse:0.928685 test-rmse:1.084627 [10] train-rmse:0.848111 test-rmse:1.055423 [11] train-rmse:0.803335 test-rmse:1.038223 [12] train-rmse:0.778426 test-rmse:1.041024 [13] train-rmse:0.762325 test-rmse:1.047040 [14] train-rmse:0.753941 test-rmse:1.052864 Stopping. Best iteration: [11] train-rmse:0.803335 test-rmse:1.038223 [1] train-rmse:9.027500 test-rmse:9.582708 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.347936 test-rmse:6.806666 [3] train-rmse:4.482496 test-rmse:4.869060 [4] train-rmse:3.191319 test-rmse:3.563491 [5] train-rmse:2.306099 test-rmse:2.692335 [6] train-rmse:1.708753 test-rmse:2.104272 [7] train-rmse:1.319152 test-rmse:1.786598 [8] train-rmse:1.075178 test-rmse:1.551226 [9] train-rmse:0.925368 test-rmse:1.412481 [10] train-rmse:0.844362 test-rmse:1.328902 [11] train-rmse:0.798847 test-rmse:1.281138 [12] train-rmse:0.776107 test-rmse:1.253701 [13] train-rmse:0.762326 test-rmse:1.232913 [14] train-rmse:0.752541 test-rmse:1.222620 [15] train-rmse:0.746077 test-rmse:1.221619 [16] train-rmse:0.742849 test-rmse:1.220666 [17] train-rmse:0.740475 test-rmse:1.217392 [18] train-rmse:0.739473 test-rmse:1.215098 [19] train-rmse:0.737054 test-rmse:1.191638 [20] train-rmse:0.734350 test-rmse:1.190613 [1] train-rmse:9.083153 test-rmse:9.060555 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.387073 test-rmse:6.359305 [3] train-rmse:4.509580 test-rmse:4.496977 [4] train-rmse:3.208633 test-rmse:3.203147 [5] train-rmse:2.316975 test-rmse:2.335459 [6] train-rmse:1.717309 test-rmse:1.769849 [7] train-rmse:1.324861 test-rmse:1.404157 [8] train-rmse:1.079189 test-rmse:1.187528 [9] train-rmse:0.932825 test-rmse:1.072122 [10] train-rmse:0.848767 test-rmse:1.007431 [11] train-rmse:0.803559 test-rmse:0.973553 [12] train-rmse:0.779757 test-rmse:0.969005 [13] train-rmse:0.763714 test-rmse:0.959313 [14] train-rmse:0.754449 test-rmse:0.956292 [15] train-rmse:0.748961 test-rmse:0.954618 [16] train-rmse:0.745733 test-rmse:0.965037 [17] train-rmse:0.743663 test-rmse:0.961958 [18] train-rmse:0.738490 test-rmse:0.957064 Stopping. Best iteration: [15] train-rmse:0.748961 test-rmse:0.954618 [1] train-rmse:9.082320 test-rmse:9.084313 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.386569 test-rmse:6.421271 [3] train-rmse:4.509357 test-rmse:4.547679 [4] train-rmse:3.209826 test-rmse:3.255306 [5] train-rmse:2.318897 test-rmse:2.388267 [6] train-rmse:1.719626 test-rmse:1.807650 [7] train-rmse:1.324714 test-rmse:1.441000 [8] train-rmse:1.077900 test-rmse:1.202433 [9] train-rmse:0.931172 test-rmse:1.058533 [10] train-rmse:0.849472 test-rmse:0.983298 [11] train-rmse:0.805389 test-rmse:0.940441 [12] train-rmse:0.778694 test-rmse:0.912229 [13] train-rmse:0.763730 test-rmse:0.901716 [14] train-rmse:0.755250 test-rmse:0.894789 [15] train-rmse:0.751309 test-rmse:0.889595 [16] train-rmse:0.747520 test-rmse:0.886695 [17] train-rmse:0.745339 test-rmse:0.886034 [18] train-rmse:0.741436 test-rmse:0.886490 [19] train-rmse:0.740126 test-rmse:0.886026 [20] train-rmse:0.739327 test-rmse:0.885733 [1] train-rmse:9.080348 test-rmse:9.021092 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.383848 test-rmse:6.302135 [3] train-rmse:4.506131 test-rmse:4.413046 [4] train-rmse:3.205190 test-rmse:3.149782 [5] train-rmse:2.312900 test-rmse:2.267292 [6] train-rmse:1.712039 test-rmse:1.703873 [7] train-rmse:1.318233 test-rmse:1.375758 [8] train-rmse:1.069531 test-rmse:1.156897 [9] train-rmse:0.922985 test-rmse:1.069590 [10] train-rmse:0.838584 test-rmse:1.036353 [11] train-rmse:0.793407 test-rmse:1.018928 [12] train-rmse:0.766563 test-rmse:1.016596 [13] train-rmse:0.752086 test-rmse:1.022038 [14] train-rmse:0.744163 test-rmse:1.026084 [15] train-rmse:0.738840 test-rmse:1.029706 Stopping. Best iteration: [12] train-rmse:0.766563 test-rmse:1.016596 [1] train-rmse:9.071086 test-rmse:9.192924 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.379179 test-rmse:6.506939 [3] train-rmse:4.505476 test-rmse:4.659128 [4] train-rmse:3.208281 test-rmse:3.361971 [5] train-rmse:2.319343 test-rmse:2.475490 [6] train-rmse:1.722562 test-rmse:1.867804 [7] train-rmse:1.332697 test-rmse:1.477445 [8] train-rmse:1.090608 test-rmse:1.232123 [9] train-rmse:0.943711 test-rmse:1.058627 [10] train-rmse:0.864423 test-rmse:0.971218 [11] train-rmse:0.816841 test-rmse:0.912911 [12] train-rmse:0.792001 test-rmse:0.882648 [13] train-rmse:0.777522 test-rmse:0.865372 [14] train-rmse:0.768071 test-rmse:0.856328 [15] train-rmse:0.762328 test-rmse:0.843595 [16] train-rmse:0.759528 test-rmse:0.840181 [17] train-rmse:0.755303 test-rmse:0.830891 [18] train-rmse:0.752225 test-rmse:0.830214 [19] train-rmse:0.749971 test-rmse:0.826682 [20] train-rmse:0.747612 test-rmse:0.826021 [1] train-rmse:9.093633 test-rmse:8.959662 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.395710 test-rmse:6.304875 [3] train-rmse:4.518446 test-rmse:4.464633 [4] train-rmse:3.218698 test-rmse:3.187976 [5] train-rmse:2.328218 test-rmse:2.319530 [6] train-rmse:1.730025 test-rmse:1.786077 [7] train-rmse:1.341226 test-rmse:1.371477 [8] train-rmse:1.096119 test-rmse:1.133211 [9] train-rmse:0.950905 test-rmse:0.983527 [10] train-rmse:0.870361 test-rmse:0.895725 [11] train-rmse:0.825032 test-rmse:0.850327 [12] train-rmse:0.799865 test-rmse:0.820974 [13] train-rmse:0.786406 test-rmse:0.799776 [14] train-rmse:0.777763 test-rmse:0.792405 [15] train-rmse:0.773469 test-rmse:0.785744 [16] train-rmse:0.768797 test-rmse:0.781672 [17] train-rmse:0.765401 test-rmse:0.785894 [18] train-rmse:0.763112 test-rmse:0.902280 [19] train-rmse:0.759985 test-rmse:0.899279 Stopping. Best iteration: [16] train-rmse:0.768797 test-rmse:0.781672 [1] train-rmse:9.112022 test-rmse:8.772576 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.409152 test-rmse:6.151186 [3] train-rmse:4.528489 test-rmse:4.332927 [4] train-rmse:3.226756 test-rmse:3.091712 [5] train-rmse:2.335254 test-rmse:2.220917 [6] train-rmse:1.735596 test-rmse:1.638434 [7] train-rmse:1.346577 test-rmse:1.247424 [8] train-rmse:1.104199 test-rmse:1.002895 [9] train-rmse:0.961285 test-rmse:0.844524 [10] train-rmse:0.881254 test-rmse:0.752730 [11] train-rmse:0.835134 test-rmse:0.698268 [12] train-rmse:0.808830 test-rmse:0.668966 [13] train-rmse:0.795049 test-rmse:0.655651 [14] train-rmse:0.786557 test-rmse:0.645085 [15] train-rmse:0.777778 test-rmse:0.640569 [16] train-rmse:0.773429 test-rmse:0.636898 [17] train-rmse:0.770430 test-rmse:0.634386 [18] train-rmse:0.767911 test-rmse:0.633892 [19] train-rmse:0.765269 test-rmse:0.632932 [20] train-rmse:0.762432 test-rmse:0.631954 [1] train-rmse:9.116705 test-rmse:8.702243 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.411930 test-rmse:6.078232 [3] train-rmse:4.528950 test-rmse:4.248223 [4] train-rmse:3.225336 test-rmse:2.957754 [5] train-rmse:2.332397 test-rmse:2.078751 [6] train-rmse:1.733054 test-rmse:1.477554 [7] train-rmse:1.339390 test-rmse:1.097560 [8] train-rmse:1.096392 test-rmse:0.889117 [9] train-rmse:0.950827 test-rmse:0.770663 [10] train-rmse:0.870788 test-rmse:0.725002 [11] train-rmse:0.825883 test-rmse:0.707745 [12] train-rmse:0.801849 test-rmse:0.705855 [13] train-rmse:0.785833 test-rmse:0.709908 [14] train-rmse:0.775833 test-rmse:0.712293 [15] train-rmse:0.771162 test-rmse:0.716947 Stopping. Best iteration: [12] train-rmse:0.801849 test-rmse:0.705855 [1] train-rmse:9.114479 test-rmse:8.785993 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.410286 test-rmse:6.236679 [3] train-rmse:4.527829 test-rmse:4.077766 [4] train-rmse:3.225001 test-rmse:2.834752 [5] train-rmse:2.333168 test-rmse:1.951186 [6] train-rmse:1.732618 test-rmse:1.439997 [7] train-rmse:1.340124 test-rmse:1.093015 [8] train-rmse:1.096732 test-rmse:0.854730 [9] train-rmse:0.948587 test-rmse:0.776537 [10] train-rmse:0.864364 test-rmse:0.755205 [11] train-rmse:0.819525 test-rmse:0.757821 [12] train-rmse:0.793511 test-rmse:0.768173 [13] train-rmse:0.780558 test-rmse:0.778803 Stopping. Best iteration: [10] train-rmse:0.864364 test-rmse:0.755205
| Feature | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| zipcode | 0.34479951 | 0.361943883 | 0.407907803 | 0.403582984 | 0.396454062 | 0.398183114 | 0.39665764 | 0.39662654 | 0.387825125 | 0.37534796 |
| residentialunits_log | 0.26060410 | 0.224631996 | 0.224358425 | 0.237877253 | 0.237904281 | 0.235338422 | 0.23473662 | 0.23420960 | 0.254247944 | 0.27177208 |
| borough | 0.14282743 | 0.128047410 | 0.147904475 | 0.163751812 | 0.162583631 | 0.171508992 | 0.15983698 | 0.15333706 | 0.148596099 | 0.13805670 |
| building_clusters | 0.13864993 | 0.148247694 | 0.107486338 | 0.104913644 | 0.080840851 | 0.105114442 | 0.07000339 | 0.10505905 | 0.106172937 | 0.11005421 |
| b_class_group_encoded | 0.04787361 | 0.080176017 | 0.055768991 | 0.045392579 | 0.067644525 | 0.035015779 | 0.07152289 | 0.04859018 | 0.050768125 | 0.04784931 |
| highly_commercial | 0.01780939 | 0.015912627 | 0.018932434 | 0.011560598 | 0.017682019 | 0.015977867 | 0.01617625 | 0.01437256 | 0.014419995 | 0.01575769 |
| taxclass_present | 0.02237428 | 0.021093100 | 0.016928498 | 0.014284079 | 0.016213037 | 0.017858443 | 0.01858911 | 0.02005562 | 0.018375934 | 0.01633516 |
| onlycommercial | 0.01266427 | 0.009166839 | 0.008371551 | 0.007320032 | 0.011253991 | 0.007732666 | 0.02012526 | 0.01346945 | 0.010035796 | 0.01267636 |
| address_encoded | 0.01239748 | 0.010780436 | 0.012341485 | 0.011317018 | 0.009423604 | 0.013270274 | 0.01235186 | 0.01427994 | 0.009558046 | 0.01215053 |
[1] "overall test rmse:"
If we remove zipcode and keep grosssquarefeet_log_group_encoded rmse increases only a little bit.
#feature_list = c("borough","zipcode","residentialunits","commercialunits","address_encoded","b_class_present_encoded","taxclassatpresent_encoded")
feature_list = c("borough","b_class_group_encoded"
,"commercialunits_group","residentialunits_group","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
,"grosssquarefeet_log_group_encoded" ## replacable with zipcode
)
target = "saleprice_log"
fit = model_xgboost(feature_list,target,chunk_no = 10)
pred_table = fit[[1]]
imp_table = fit[[2]]
imp_table
## target = saleprice
print("overall test rmse:")
calc_rmse(pred_table$pred,pred_table$actual)
calc_rmse(pred_table[actual < 20000000]$pred,pred_table[actual < 20000000]$actual)
[1] train-rmse:9.015265 test-rmse:9.654713 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.340294 test-rmse:6.812314 [3] train-rmse:4.478897 test-rmse:4.834664 [4] train-rmse:3.189979 test-rmse:3.487850 [5] train-rmse:2.307970 test-rmse:2.571551 [6] train-rmse:1.716564 test-rmse:1.961449 [7] train-rmse:1.333268 test-rmse:1.575325 [8] train-rmse:1.096979 test-rmse:1.349704 [9] train-rmse:0.958209 test-rmse:1.211321 [10] train-rmse:0.881858 test-rmse:1.136287 [11] train-rmse:0.840173 test-rmse:1.099157 [12] train-rmse:0.818386 test-rmse:1.081450 [13] train-rmse:0.807365 test-rmse:1.071248 [14] train-rmse:0.800842 test-rmse:1.067611 [15] train-rmse:0.797501 test-rmse:1.065767 [16] train-rmse:0.795212 test-rmse:1.063245 [17] train-rmse:0.793646 test-rmse:1.063488 [18] train-rmse:0.792645 test-rmse:1.063297 [19] train-rmse:0.791472 test-rmse:1.067395 Stopping. Best iteration: [16] train-rmse:0.795212 test-rmse:1.063245 [1] train-rmse:9.028257 test-rmse:9.541186 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.349397 test-rmse:6.727898 [3] train-rmse:4.485528 test-rmse:4.769876 [4] train-rmse:3.194750 test-rmse:3.427783 [5] train-rmse:2.311609 test-rmse:2.527247 [6] train-rmse:1.719703 test-rmse:1.939452 [7] train-rmse:1.336055 test-rmse:1.569207 [8] train-rmse:1.099086 test-rmse:1.349633 [9] train-rmse:0.959428 test-rmse:1.226429 [10] train-rmse:0.882760 test-rmse:1.160795 [11] train-rmse:0.841739 test-rmse:1.127290 [12] train-rmse:0.820290 test-rmse:1.110139 [13] train-rmse:0.809055 test-rmse:1.109181 [14] train-rmse:0.803171 test-rmse:1.104919 [15] train-rmse:0.799806 test-rmse:1.104101 [16] train-rmse:0.797874 test-rmse:1.102719 [17] train-rmse:0.796597 test-rmse:1.108824 [18] train-rmse:0.795642 test-rmse:1.109984 [19] train-rmse:0.794976 test-rmse:1.110763 Stopping. Best iteration: [16] train-rmse:0.797874 test-rmse:1.102719 [1] train-rmse:9.084407 test-rmse:8.953101 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.389534 test-rmse:6.256651 [3] train-rmse:4.514002 test-rmse:4.437208 [4] train-rmse:3.216899 test-rmse:3.176941 [5] train-rmse:2.329518 test-rmse:2.296787 [6] train-rmse:1.735268 test-rmse:1.730135 [7] train-rmse:1.350576 test-rmse:1.370702 [8] train-rmse:1.113801 test-rmse:1.161716 [9] train-rmse:0.975829 test-rmse:1.054815 [10] train-rmse:0.899972 test-rmse:1.000587 [11] train-rmse:0.859688 test-rmse:0.973638 [12] train-rmse:0.837914 test-rmse:0.953883 [13] train-rmse:0.825868 test-rmse:0.949774 [14] train-rmse:0.819726 test-rmse:0.942129 [15] train-rmse:0.816720 test-rmse:0.942135 [16] train-rmse:0.814287 test-rmse:0.946258 [17] train-rmse:0.812921 test-rmse:0.946043 Stopping. Best iteration: [14] train-rmse:0.819726 test-rmse:0.942129 [1] train-rmse:9.083746 test-rmse:9.105793 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.389119 test-rmse:6.445896 [3] train-rmse:4.513856 test-rmse:4.596087 [4] train-rmse:3.216558 test-rmse:3.296506 [5] train-rmse:2.329731 test-rmse:2.400632 [6] train-rmse:1.735380 test-rmse:1.812395 [7] train-rmse:1.350976 test-rmse:1.422088 [8] train-rmse:1.114577 test-rmse:1.180236 [9] train-rmse:0.976728 test-rmse:1.039690 [10] train-rmse:0.901709 test-rmse:0.963021 [11] train-rmse:0.860486 test-rmse:0.921594 [12] train-rmse:0.838498 test-rmse:0.898367 [13] train-rmse:0.827412 test-rmse:0.886984 [14] train-rmse:0.821181 test-rmse:0.880133 [15] train-rmse:0.818067 test-rmse:0.876669 [16] train-rmse:0.815734 test-rmse:0.874485 [17] train-rmse:0.813941 test-rmse:0.872983 [18] train-rmse:0.813059 test-rmse:0.872512 [19] train-rmse:0.812239 test-rmse:0.871940 [20] train-rmse:0.811375 test-rmse:0.872143 [1] train-rmse:9.081690 test-rmse:9.068726 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.386729 test-rmse:6.373392 [3] train-rmse:4.510906 test-rmse:4.523714 [4] train-rmse:3.211977 test-rmse:3.197373 [5] train-rmse:2.323440 test-rmse:2.336447 [6] train-rmse:1.727258 test-rmse:1.744716 [7] train-rmse:1.340233 test-rmse:1.387193 [8] train-rmse:1.099712 test-rmse:1.189337 [9] train-rmse:0.960279 test-rmse:1.088948 [10] train-rmse:0.881617 test-rmse:1.044854 [11] train-rmse:0.839729 test-rmse:1.026744 [12] train-rmse:0.818339 test-rmse:1.022736 [13] train-rmse:0.806347 test-rmse:1.023781 [14] train-rmse:0.800483 test-rmse:1.026869 [15] train-rmse:0.797191 test-rmse:1.028371 Stopping. Best iteration: [12] train-rmse:0.818339 test-rmse:1.022736 [1] train-rmse:9.072309 test-rmse:9.240442 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.381633 test-rmse:6.549593 [3] train-rmse:4.509710 test-rmse:4.694054 [4] train-rmse:3.215288 test-rmse:3.409637 [5] train-rmse:2.330758 test-rmse:2.515824 [6] train-rmse:1.738381 test-rmse:1.894192 [7] train-rmse:1.356297 test-rmse:1.482674 [8] train-rmse:1.120924 test-rmse:1.226079 [9] train-rmse:0.984278 test-rmse:1.068846 [10] train-rmse:0.909267 test-rmse:0.976058 [11] train-rmse:0.867713 test-rmse:0.915232 [12] train-rmse:0.847414 test-rmse:0.882554 [13] train-rmse:0.835613 test-rmse:0.858031 [14] train-rmse:0.829878 test-rmse:0.845132 [15] train-rmse:0.826299 test-rmse:0.838711 [16] train-rmse:0.823948 test-rmse:0.833451 [17] train-rmse:0.822226 test-rmse:0.830552 [18] train-rmse:0.821005 test-rmse:0.828330 [19] train-rmse:0.820124 test-rmse:0.826718 [20] train-rmse:0.819419 test-rmse:0.825055 [1] train-rmse:9.095226 test-rmse:9.030133 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.398825 test-rmse:6.412409 [3] train-rmse:4.522441 test-rmse:4.629395 [4] train-rmse:3.224946 test-rmse:3.355708 [5] train-rmse:2.338196 test-rmse:2.495261 [6] train-rmse:1.745337 test-rmse:1.908586 [7] train-rmse:1.361809 test-rmse:1.513069 [8] train-rmse:1.126494 test-rmse:1.243944 [9] train-rmse:0.988679 test-rmse:1.080339 [10] train-rmse:0.912107 test-rmse:0.983555 [11] train-rmse:0.871721 test-rmse:0.925539 [12] train-rmse:0.850242 test-rmse:0.888019 [13] train-rmse:0.839312 test-rmse:0.865418 [14] train-rmse:0.833744 test-rmse:0.851341 [15] train-rmse:0.830256 test-rmse:0.839774 [16] train-rmse:0.828435 test-rmse:0.838219 [17] train-rmse:0.826671 test-rmse:0.834997 [18] train-rmse:0.825837 test-rmse:0.832644 [19] train-rmse:0.825010 test-rmse:0.830086 [20] train-rmse:0.824286 test-rmse:0.826713 [1] train-rmse:9.113760 test-rmse:8.807259 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.412152 test-rmse:6.212079 [3] train-rmse:4.533429 test-rmse:4.364237 [4] train-rmse:3.233538 test-rmse:3.105512 [5] train-rmse:2.344756 test-rmse:2.240943 [6] train-rmse:1.750989 test-rmse:1.647199 [7] train-rmse:1.366870 test-rmse:1.255251 [8] train-rmse:1.131346 test-rmse:1.001489 [9] train-rmse:0.994912 test-rmse:0.849514 [10] train-rmse:0.919913 test-rmse:0.763950 [11] train-rmse:0.879125 test-rmse:0.716065 [12] train-rmse:0.858078 test-rmse:0.688849 [13] train-rmse:0.846881 test-rmse:0.675464 [14] train-rmse:0.841158 test-rmse:0.668608 [15] train-rmse:0.837735 test-rmse:0.666313 [16] train-rmse:0.835231 test-rmse:0.664364 [17] train-rmse:0.834064 test-rmse:0.663440 [18] train-rmse:0.833257 test-rmse:0.663014 [19] train-rmse:0.832408 test-rmse:0.662555 [20] train-rmse:0.831767 test-rmse:0.662410 [1] train-rmse:9.117471 test-rmse:8.622677 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.413403 test-rmse:5.976238 [3] train-rmse:4.532607 test-rmse:4.132426 [4] train-rmse:3.231642 test-rmse:2.856329 [5] train-rmse:2.342413 test-rmse:1.962888 [6] train-rmse:1.746981 test-rmse:1.390268 [7] train-rmse:1.362788 test-rmse:1.036103 [8] train-rmse:1.125683 test-rmse:0.839297 [9] train-rmse:0.988330 test-rmse:0.752868 [10] train-rmse:0.911420 test-rmse:0.723373 [11] train-rmse:0.870845 test-rmse:0.721724 [12] train-rmse:0.849941 test-rmse:0.730770 [13] train-rmse:0.839335 test-rmse:0.740951 [14] train-rmse:0.833109 test-rmse:0.751114 Stopping. Best iteration: [11] train-rmse:0.870845 test-rmse:0.721724 [1] train-rmse:9.115537 test-rmse:8.638844 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.412427 test-rmse:5.982539 [3] train-rmse:4.531507 test-rmse:4.138980 [4] train-rmse:3.231367 test-rmse:2.851833 [5] train-rmse:2.343301 test-rmse:1.969012 [6] train-rmse:1.748819 test-rmse:1.395690 [7] train-rmse:1.365413 test-rmse:1.067132 [8] train-rmse:1.130150 test-rmse:0.871948 [9] train-rmse:0.994500 test-rmse:0.787551 [10] train-rmse:0.919068 test-rmse:0.746279 [11] train-rmse:0.878456 test-rmse:0.732303 [12] train-rmse:0.857597 test-rmse:0.734462 [13] train-rmse:0.845912 test-rmse:0.738015 [14] train-rmse:0.840244 test-rmse:0.744387 Stopping. Best iteration: [11] train-rmse:0.878456 test-rmse:0.732303
| Feature | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| borough | 0.32314555 | 0.34634116 | 0.40650885 | 0.43506594 | 0.42809053 | 0.43794265 | 0.41683882 | 0.40263442 | 0.39882561 | 0.37593176 |
| taxclass_present | 0.19727050 | 0.16997872 | 0.15992558 | 0.15396464 | 0.14967387 | 0.12819648 | 0.07843711 | 0.06912372 | 0.15803424 | 0.16778050 |
| grosssquarefeet_log_group_encoded | 0.17238952 | 0.15778050 | 0.12605381 | 0.11121754 | 0.11755838 | 0.11993267 | 0.12335037 | 0.12687699 | 0.12436720 | 0.12816887 |
| onlycommercial | 0.13262527 | 0.08657659 | 0.08523277 | 0.09882453 | 0.10457746 | 0.09966909 | 0.08693106 | 0.08819079 | 0.10916972 | 0.12813700 |
| building_clusters | 0.01809992 | 0.06579880 | 0.08450339 | 0.07419227 | 0.07203429 | 0.07780739 | 0.07995502 | 0.08151238 | 0.07651454 | 0.07059309 |
| highly_commercial | 0.04109750 | 0.02881136 | 0.02987213 | 0.02868328 | 0.03697382 | 0.02597311 | 0.03810978 | 0.03337971 | 0.02953910 | 0.03018972 |
| residentialunits_group | 0.03436199 | 0.04560094 | 0.02866140 | 0.03424093 | 0.03209310 | 0.03171803 | 0.05007354 | 0.05491468 | 0.03081090 | 0.02522330 |
| commercialunits_group | 0.01736617 | 0.03700198 | 0.02749259 | 0.02377227 | 0.02312809 | 0.02977925 | 0.05442819 | 0.05645065 | 0.02446366 | 0.03768015 |
| b_class_group_encoded | 0.04696046 | 0.04659738 | 0.02724243 | 0.02400957 | 0.02225546 | 0.03035703 | 0.05554338 | 0.06918672 | 0.03328478 | 0.02328792 |
| address_encoded | 0.01668312 | 0.01551257 | 0.02450706 | 0.01602904 | 0.01361500 | 0.01862428 | 0.01633273 | 0.01772993 | 0.01499026 | 0.01300769 |
[1] "overall test rmse:"
options(repr.plot.width = 10, repr.plot.height = 5, repr.plot.res = 200) # for graph sizes
p1 = ggplot(data = pred_table, aes(x = actual, y = pred, color = as.factor(chunk))) + geom_point() + geom_abline(intercept = 0, slope = 1)+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
p2 = ggplot(data = pred_table[actual < 20000000], aes(x = actual, y = pred, color = as.factor(chunk))) + geom_point() + geom_abline(intercept = 0, slope = 1) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
grid.arrange(p1,p2, ncol = 2)
Based on borough
options(repr.plot.width = 10, repr.plot.height = 5, repr.plot.res = 200) # for graph sizes
ggplot(data = pred_table[actual < 20000000,.(actual, pred, borough = dt[saleprice < 20000000]$borough)]
, aes(x = actual, y = pred, color = as.factor(borough))) + geom_point() + geom_abline(intercept = 0, slope = 1) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
Warning message in as.data.table.list(jval, .named = NULL): "Item 3 has 57439 rows but longest item has 57445; recycled with remainder."
Predictions are generally below actual sale prices. It seems that outliers in the training set causes problems.
But before that, lets try to address each borough and building class pair separately.
If we study partial models, it is important that we eliminated the outliers because with target = saleprice, rmse increases drastically.
feature_list = c( "zipcode","commercialunits_group","residentialunits_group","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
)
target = "saleprice"
fit_bb = model_xgboost_partial(feature_list,target,chunk_no = 5)
pred_table_bb = fit_bb[[1]]
imp_table_bb = fit_bb[[2]]
imp_table_bb
## target = saleprice
print("overall test rmse:")
calc_rmse(pred_table_bb$pred,pred_table_bb$actual)
calc_rmse(pred_table_bb[actual < 20000000]$pred,pred_table_bb[actual < 20000000]$actual)
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:5713704.500000 test-rmse:9591128.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4919245.500000 test-rmse:9290110.000000 [3] train-rmse:4408553.000000 test-rmse:9133765.000000 [4] train-rmse:4009983.000000 test-rmse:9001239.000000 [5] train-rmse:3759177.500000 test-rmse:8954834.000000 [6] train-rmse:3552701.750000 test-rmse:8928165.000000 [7] train-rmse:3405038.000000 test-rmse:8920197.000000 [8] train-rmse:3300301.250000 test-rmse:8884942.000000 [9] train-rmse:3234957.500000 test-rmse:8886349.000000 [10] train-rmse:3179999.250000 test-rmse:8893731.000000 [11] train-rmse:3120129.750000 test-rmse:8893089.000000 Stopping. Best iteration: [8] train-rmse:3300301.250000 test-rmse:8884942.000000 [1] train-rmse:6388114.000000 test-rmse:6517715.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5448325.000000 test-rmse:6280355.000000 [3] train-rmse:4745909.500000 test-rmse:6570078.000000 [4] train-rmse:4224777.500000 test-rmse:7018766.000000 [5] train-rmse:3837713.250000 test-rmse:7605943.000000 Stopping. Best iteration: [2] train-rmse:5448325.000000 test-rmse:6280355.000000 [1] train-rmse:6038301.500000 test-rmse:8145653.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5129710.500000 test-rmse:7652327.500000 [3] train-rmse:4425636.500000 test-rmse:7381347.000000 [4] train-rmse:3881015.000000 test-rmse:7298161.000000 [5] train-rmse:3460643.500000 test-rmse:7272546.500000 [6] train-rmse:3122050.250000 test-rmse:7239485.000000 [7] train-rmse:2856780.750000 test-rmse:7218218.000000 [8] train-rmse:2650727.000000 test-rmse:7199650.500000 [9] train-rmse:2494878.750000 test-rmse:7198750.000000 [10] train-rmse:2371198.000000 test-rmse:7204999.000000 [11] train-rmse:2284109.000000 test-rmse:7218601.500000 [12] train-rmse:2210466.750000 test-rmse:7232583.000000 Stopping. Best iteration: [9] train-rmse:2494878.750000 test-rmse:7198750.000000 [1] train-rmse:6658707.000000 test-rmse:5333071.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5682669.500000 test-rmse:4841299.000000 [3] train-rmse:4985075.500000 test-rmse:4570841.500000 [4] train-rmse:4469633.500000 test-rmse:4415545.000000 [5] train-rmse:3995756.500000 test-rmse:4338903.500000 [6] train-rmse:3631673.500000 test-rmse:4493000.000000 [7] train-rmse:3347654.000000 test-rmse:4665230.500000 [8] train-rmse:3136731.750000 test-rmse:4878201.000000 Stopping. Best iteration: [5] train-rmse:3995756.500000 test-rmse:4338903.500000 [1] train-rmse:6955627.500000 test-rmse:3265782.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5944331.500000 test-rmse:2602564.250000 [3] train-rmse:5287847.000000 test-rmse:2338916.750000 [4] train-rmse:4672929.500000 test-rmse:2142552.500000 [5] train-rmse:4212189.000000 test-rmse:2084831.750000 [6] train-rmse:3836109.000000 test-rmse:2044079.875000 [7] train-rmse:3555048.500000 test-rmse:2026159.250000 [8] train-rmse:3357342.750000 test-rmse:2000427.000000 [9] train-rmse:3204286.500000 test-rmse:1997858.375000 [10] train-rmse:3091406.750000 test-rmse:2019546.500000 [11] train-rmse:3010039.000000 test-rmse:2017480.125000 [12] train-rmse:2912622.750000 test-rmse:2013511.375000 Stopping. Best iteration: [9] train-rmse:3204286.500000 test-rmse:1997858.375000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8938046.000000 test-rmse:10163435.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7632638.500000 test-rmse:12163311.000000 [3] train-rmse:6554452.500000 test-rmse:14727506.000000 [4] train-rmse:5662910.000000 test-rmse:17117018.000000 Stopping. Best iteration: [1] train-rmse:8938046.000000 test-rmse:10163435.000000 [1] train-rmse:5376536.000000 test-rmse:19082548.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4534164.000000 test-rmse:19002100.000000 [3] train-rmse:3898312.500000 test-rmse:18973668.000000 [4] train-rmse:3413629.500000 test-rmse:18987814.000000 [5] train-rmse:3053340.750000 test-rmse:19018570.000000 [6] train-rmse:2785515.500000 test-rmse:19044254.000000 Stopping. Best iteration: [3] train-rmse:3898312.500000 test-rmse:18973668.000000 [1] train-rmse:9660462.000000 test-rmse:3308602.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:8199779.000000 test-rmse:3146871.500000 [3] train-rmse:7014280.000000 test-rmse:3064090.000000 [4] train-rmse:5994240.500000 test-rmse:3016474.500000 [5] train-rmse:5155731.000000 test-rmse:3000395.750000 [6] train-rmse:4491828.500000 test-rmse:2980911.250000 [7] train-rmse:3926933.500000 test-rmse:2982358.750000 [8] train-rmse:3472228.000000 test-rmse:2992672.250000 [9] train-rmse:3103242.500000 test-rmse:2994276.750000 Stopping. Best iteration: [6] train-rmse:4491828.500000 test-rmse:2980911.250000 [1] train-rmse:9765936.000000 test-rmse:2308026.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:8269490.500000 test-rmse:2149842.250000 [3] train-rmse:7044569.500000 test-rmse:2194830.500000 [4] train-rmse:6043910.000000 test-rmse:2213288.500000 [5] train-rmse:5223816.500000 test-rmse:2307382.250000 Stopping. Best iteration: [2] train-rmse:8269490.500000 test-rmse:2149842.250000 [1] train-rmse:9311211.000000 test-rmse:6771091.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7882771.000000 test-rmse:6562852.000000 [3] train-rmse:6711334.500000 test-rmse:6462256.500000 [4] train-rmse:5753424.500000 test-rmse:6299619.500000 [5] train-rmse:4961780.500000 test-rmse:6266370.500000 [6] train-rmse:4318510.500000 test-rmse:6274495.000000 [7] train-rmse:3796689.500000 test-rmse:6289257.500000 [8] train-rmse:3371967.500000 test-rmse:6297253.500000 Stopping. Best iteration: [5] train-rmse:4961780.500000 test-rmse:6266370.500000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:6040739.500000 test-rmse:5613885.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5731368.000000 test-rmse:5321536.000000 [3] train-rmse:5565285.000000 test-rmse:5180362.000000 [4] train-rmse:5409315.500000 test-rmse:5146421.500000 [5] train-rmse:5350009.000000 test-rmse:5151418.000000 [6] train-rmse:5278820.500000 test-rmse:5187229.000000 [7] train-rmse:5246700.000000 test-rmse:5230003.500000 Stopping. Best iteration: [4] train-rmse:5409315.500000 test-rmse:5146421.500000 [1] train-rmse:6152971.500000 test-rmse:5413611.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5886792.500000 test-rmse:5157612.000000 [3] train-rmse:5646637.500000 test-rmse:5013506.500000 [4] train-rmse:5562035.500000 test-rmse:4873689.500000 [5] train-rmse:5433150.500000 test-rmse:4838464.000000 [6] train-rmse:5396291.000000 test-rmse:4776355.500000 [7] train-rmse:5341079.000000 test-rmse:4786857.000000 [8] train-rmse:5323837.500000 test-rmse:4761509.500000 [9] train-rmse:5295825.000000 test-rmse:4802238.000000 [10] train-rmse:5287259.500000 test-rmse:4788371.000000 [11] train-rmse:5278053.500000 test-rmse:4799753.500000 Stopping. Best iteration: [8] train-rmse:5323837.500000 test-rmse:4761509.500000 [1] train-rmse:5240260.500000 test-rmse:8177897.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4867108.000000 test-rmse:8069902.500000 [3] train-rmse:4640217.500000 test-rmse:8037940.500000 [4] train-rmse:4515372.000000 test-rmse:8034086.500000 [5] train-rmse:4436727.000000 test-rmse:8015478.000000 [6] train-rmse:4374495.500000 test-rmse:8048019.000000 [7] train-rmse:4318483.500000 test-rmse:8052926.000000 [8] train-rmse:4295136.500000 test-rmse:8061008.000000 Stopping. Best iteration: [5] train-rmse:4436727.000000 test-rmse:8015478.000000 [1] train-rmse:5994120.500000 test-rmse:5981865.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5592839.000000 test-rmse:5646468.500000 [3] train-rmse:5397275.000000 test-rmse:5503788.000000 [4] train-rmse:5289197.000000 test-rmse:5431810.000000 [5] train-rmse:5232522.500000 test-rmse:5408669.500000 [6] train-rmse:5183746.000000 test-rmse:5400695.500000 [7] train-rmse:5153287.000000 test-rmse:5404011.000000 [8] train-rmse:5138584.500000 test-rmse:5402866.000000 [9] train-rmse:5104026.500000 test-rmse:5403874.500000 Stopping. Best iteration: [6] train-rmse:5183746.000000 test-rmse:5400695.500000 [1] train-rmse:6170357.000000 test-rmse:4743316.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5798398.000000 test-rmse:4629371.000000 [3] train-rmse:5595220.000000 test-rmse:4499640.500000 [4] train-rmse:5482261.000000 test-rmse:4553890.000000 [5] train-rmse:5414295.500000 test-rmse:4523917.000000 [6] train-rmse:5359398.500000 test-rmse:4648766.000000 Stopping. Best iteration: [3] train-rmse:5595220.000000 test-rmse:4499640.500000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:119696352.000000 test-rmse:165644368.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:102578400.000000 test-rmse:163231376.000000 [3] train-rmse:88399968.000000 test-rmse:161468800.000000 [4] train-rmse:76445984.000000 test-rmse:160470064.000000 [5] train-rmse:66703176.000000 test-rmse:158864192.000000 [6] train-rmse:58742452.000000 test-rmse:157864048.000000 [7] train-rmse:52039008.000000 test-rmse:157490192.000000 [8] train-rmse:46751676.000000 test-rmse:157244960.000000 [9] train-rmse:42109248.000000 test-rmse:155362048.000000 [10] train-rmse:38484016.000000 test-rmse:155129488.000000 [11] train-rmse:35565996.000000 test-rmse:155021856.000000 [12] train-rmse:33352072.000000 test-rmse:154899344.000000 [13] train-rmse:31588516.000000 test-rmse:154819200.000000 [14] train-rmse:30302674.000000 test-rmse:154811616.000000 [15] train-rmse:29293676.000000 test-rmse:154672448.000000 [16] train-rmse:28268500.000000 test-rmse:154600960.000000 [17] train-rmse:27700960.000000 test-rmse:154594176.000000 [18] train-rmse:26973142.000000 test-rmse:154509904.000000 [19] train-rmse:26593466.000000 test-rmse:154496480.000000 [20] train-rmse:26299816.000000 test-rmse:154569760.000000 [1] train-rmse:136985408.000000 test-rmse:47628144.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:116778992.000000 test-rmse:52327124.000000 [3] train-rmse:100456208.000000 test-rmse:59506852.000000 [4] train-rmse:87260704.000000 test-rmse:67111136.000000 Stopping. Best iteration: [1] train-rmse:136985408.000000 test-rmse:47628144.000000 [1] train-rmse:77572152.000000 test-rmse:268202784.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:66399844.000000 test-rmse:266983392.000000 [3] train-rmse:58060772.000000 test-rmse:266689264.000000 [4] train-rmse:51933696.000000 test-rmse:266240704.000000 [5] train-rmse:47555544.000000 test-rmse:266146912.000000 [6] train-rmse:44096176.000000 test-rmse:266048544.000000 [7] train-rmse:41546728.000000 test-rmse:266021488.000000 [8] train-rmse:39571120.000000 test-rmse:266438480.000000 [9] train-rmse:38007356.000000 test-rmse:266780656.000000 [10] train-rmse:36780888.000000 test-rmse:266805360.000000 Stopping. Best iteration: [7] train-rmse:41546728.000000 test-rmse:266021488.000000 [1] train-rmse:137815792.000000 test-rmse:36426412.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:117638016.000000 test-rmse:35822840.000000 [3] train-rmse:101339984.000000 test-rmse:35544892.000000 [4] train-rmse:88258032.000000 test-rmse:35710728.000000 [5] train-rmse:77635176.000000 test-rmse:35646176.000000 [6] train-rmse:68907152.000000 test-rmse:35920244.000000 Stopping. Best iteration: [3] train-rmse:101339984.000000 test-rmse:35544892.000000 [1] train-rmse:138214096.000000 test-rmse:105842072.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:117908256.000000 test-rmse:192443232.000000 [3] train-rmse:101487656.000000 test-rmse:267596048.000000 [4] train-rmse:88327136.000000 test-rmse:331650624.000000 Stopping. Best iteration: [1] train-rmse:138214096.000000 test-rmse:105842072.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9405757.000000 test-rmse:10219531.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7925381.000000 test-rmse:9555580.000000 [3] train-rmse:6976979.500000 test-rmse:9039661.000000 [4] train-rmse:6397596.500000 test-rmse:7914185.000000 [5] train-rmse:6028932.500000 test-rmse:7700378.000000 [6] train-rmse:5810976.000000 test-rmse:7515632.500000 [7] train-rmse:5684246.000000 test-rmse:7331613.000000 [8] train-rmse:5613926.500000 test-rmse:7184148.000000 [9] train-rmse:5569502.000000 test-rmse:6921661.000000 [10] train-rmse:5545552.500000 test-rmse:6841195.000000 [11] train-rmse:5530642.000000 test-rmse:6721123.000000 [12] train-rmse:5520577.500000 test-rmse:6693953.500000 [13] train-rmse:5513828.000000 test-rmse:6617387.500000 [14] train-rmse:5509716.500000 test-rmse:6598136.000000 [15] train-rmse:5507169.500000 test-rmse:6590658.000000 [16] train-rmse:5505626.000000 test-rmse:6580923.000000 [17] train-rmse:5504604.000000 test-rmse:6556415.000000 [18] train-rmse:5503976.000000 test-rmse:6553122.500000 [19] train-rmse:5503541.500000 test-rmse:6548409.000000 [20] train-rmse:5503260.500000 test-rmse:6544612.500000 [1] train-rmse:10232588.000000 test-rmse:1609902.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:8477969.000000 test-rmse:1500941.875000 [3] train-rmse:7354641.500000 test-rmse:1557479.625000 [4] train-rmse:6673603.000000 test-rmse:1645190.250000 [5] train-rmse:6257002.500000 test-rmse:1817469.750000 Stopping. Best iteration: [2] train-rmse:8477969.000000 test-rmse:1500941.875000 [1] train-rmse:6949533.500000 test-rmse:15896235.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5523845.000000 test-rmse:14338075.000000 [3] train-rmse:4565186.500000 test-rmse:13570959.000000 [4] train-rmse:3926978.750000 test-rmse:13185392.000000 [5] train-rmse:3463083.750000 test-rmse:12738232.000000 [6] train-rmse:3159926.250000 test-rmse:12578298.000000 [7] train-rmse:2912887.000000 test-rmse:12432054.000000 [8] train-rmse:2747419.000000 test-rmse:12380072.000000 [9] train-rmse:2638010.500000 test-rmse:12365596.000000 [10] train-rmse:2572265.500000 test-rmse:12336484.000000 [11] train-rmse:2508798.000000 test-rmse:12320246.000000 [12] train-rmse:2466423.750000 test-rmse:12276664.000000 [13] train-rmse:2436412.000000 test-rmse:12254730.000000 [14] train-rmse:2414183.500000 test-rmse:12236258.000000 [15] train-rmse:2397110.250000 test-rmse:12223921.000000 [16] train-rmse:2386049.750000 test-rmse:12215879.000000 [17] train-rmse:2378088.250000 test-rmse:12213084.000000 [18] train-rmse:2371482.500000 test-rmse:12234439.000000 [19] train-rmse:2366864.750000 test-rmse:12251203.000000 [20] train-rmse:2362659.000000 test-rmse:12258733.000000 Stopping. Best iteration: [17] train-rmse:2378088.250000 test-rmse:12213084.000000 [1] train-rmse:9393421.000000 test-rmse:8257606.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7837153.500000 test-rmse:6843292.500000 [3] train-rmse:6789692.500000 test-rmse:6363250.000000 [4] train-rmse:6131990.500000 test-rmse:6257471.500000 [5] train-rmse:5722895.000000 test-rmse:6334122.500000 [6] train-rmse:5467315.500000 test-rmse:6420461.500000 [7] train-rmse:5312601.000000 test-rmse:6536760.000000 Stopping. Best iteration: [4] train-rmse:6131990.500000 test-rmse:6257471.500000 [1] train-rmse:9502942.000000 test-rmse:7676457.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7978059.000000 test-rmse:7166100.000000 [3] train-rmse:6981632.500000 test-rmse:6833715.500000 [4] train-rmse:6381647.000000 test-rmse:6604392.500000 [5] train-rmse:5989351.500000 test-rmse:5560876.500000 [6] train-rmse:5754526.500000 test-rmse:4876206.500000 [7] train-rmse:5629355.000000 test-rmse:4584236.500000 [8] train-rmse:5526196.500000 test-rmse:4516544.000000 [9] train-rmse:5449321.000000 test-rmse:4438534.000000 [10] train-rmse:5398616.500000 test-rmse:4306527.000000 [11] train-rmse:5363443.500000 test-rmse:4247549.500000 [12] train-rmse:5340357.500000 test-rmse:4213048.500000 [13] train-rmse:5325137.500000 test-rmse:4209341.500000 [14] train-rmse:5314476.500000 test-rmse:4199849.500000 [15] train-rmse:5306914.500000 test-rmse:4175319.750000 [16] train-rmse:5301667.500000 test-rmse:4157670.000000 [17] train-rmse:5298009.000000 test-rmse:4154076.250000 [18] train-rmse:5294904.000000 test-rmse:4152539.000000 [19] train-rmse:5293031.500000 test-rmse:4143128.750000 [20] train-rmse:5291348.500000 test-rmse:4142651.750000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:4969931.000000 test-rmse:6960448.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3971531.500000 test-rmse:6296238.000000 [3] train-rmse:3230871.500000 test-rmse:5812261.500000 [4] train-rmse:2682731.000000 test-rmse:5659360.500000 [5] train-rmse:2283322.250000 test-rmse:5106778.000000 [6] train-rmse:1975804.500000 test-rmse:5078865.500000 [7] train-rmse:1754765.875000 test-rmse:5063322.000000 [8] train-rmse:1592207.250000 test-rmse:5056497.500000 [9] train-rmse:1477436.750000 test-rmse:5055735.500000 [10] train-rmse:1400949.500000 test-rmse:4980608.000000 [11] train-rmse:1348519.500000 test-rmse:4922331.000000 [12] train-rmse:1309186.625000 test-rmse:4918497.500000 [13] train-rmse:1280081.125000 test-rmse:4944883.000000 [14] train-rmse:1259330.125000 test-rmse:4969106.500000 [15] train-rmse:1244619.750000 test-rmse:4990643.000000 Stopping. Best iteration: [12] train-rmse:1309186.625000 test-rmse:4918497.500000 [1] train-rmse:5669304.500000 test-rmse:1481558.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4632839.000000 test-rmse:2423782.250000 [3] train-rmse:3809812.000000 test-rmse:2041142.125000 [4] train-rmse:3198253.250000 test-rmse:1730100.125000 Stopping. Best iteration: [1] train-rmse:5669304.500000 test-rmse:1481558.750000 [1] train-rmse:5641077.000000 test-rmse:1950386.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4540218.000000 test-rmse:1649910.875000 [3] train-rmse:3729388.750000 test-rmse:1473460.250000 [4] train-rmse:3162059.000000 test-rmse:1383574.000000 [5] train-rmse:2744038.500000 test-rmse:1324178.250000 [6] train-rmse:2420146.000000 test-rmse:1302650.625000 [7] train-rmse:2199677.500000 test-rmse:1268629.500000 [8] train-rmse:2039421.875000 test-rmse:1271106.500000 [9] train-rmse:1933034.750000 test-rmse:1263234.125000 [10] train-rmse:1851669.750000 test-rmse:1257255.125000 [11] train-rmse:1797232.125000 test-rmse:1252513.250000 [12] train-rmse:1757324.250000 test-rmse:1261516.750000 [13] train-rmse:1719888.125000 test-rmse:1257481.625000 [14] train-rmse:1693842.375000 test-rmse:1264611.750000 Stopping. Best iteration: [11] train-rmse:1797232.125000 test-rmse:1252513.250000 [1] train-rmse:4487155.000000 test-rmse:7848956.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3660141.250000 test-rmse:6702026.000000 [3] train-rmse:3046183.750000 test-rmse:5811544.500000 [4] train-rmse:2605218.750000 test-rmse:5474302.000000 [5] train-rmse:2287096.250000 test-rmse:5070987.500000 [6] train-rmse:2050108.000000 test-rmse:4928562.000000 [7] train-rmse:1869135.125000 test-rmse:4818986.500000 [8] train-rmse:1735327.125000 test-rmse:4774422.500000 [9] train-rmse:1636424.875000 test-rmse:4742385.500000 [10] train-rmse:1564530.125000 test-rmse:4721418.000000 [11] train-rmse:1511960.375000 test-rmse:4720907.000000 [12] train-rmse:1473667.375000 test-rmse:4723027.000000 [13] train-rmse:1445905.625000 test-rmse:4732152.000000 [14] train-rmse:1425790.125000 test-rmse:4740125.500000 Stopping. Best iteration: [11] train-rmse:1511960.375000 test-rmse:4720907.000000 [1] train-rmse:4851530.000000 test-rmse:6936941.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3862602.500000 test-rmse:6547519.500000 [3] train-rmse:3166030.250000 test-rmse:6294621.500000 [4] train-rmse:2698782.750000 test-rmse:6107394.000000 [5] train-rmse:2339856.750000 test-rmse:5978801.500000 [6] train-rmse:2096466.000000 test-rmse:5880481.000000 [7] train-rmse:1935080.250000 test-rmse:5820528.000000 [8] train-rmse:1838597.625000 test-rmse:5777225.000000 [9] train-rmse:1766800.375000 test-rmse:5746453.500000 [10] train-rmse:1719733.250000 test-rmse:5724305.000000 [11] train-rmse:1688394.250000 test-rmse:5712813.000000 [12] train-rmse:1667804.875000 test-rmse:5704293.500000 [13] train-rmse:1654064.875000 test-rmse:5695371.500000 [14] train-rmse:1644832.125000 test-rmse:5691013.000000 [15] train-rmse:1638695.125000 test-rmse:5686126.000000 [16] train-rmse:1634513.625000 test-rmse:5681256.500000 [17] train-rmse:1631588.375000 test-rmse:5677696.500000 [18] train-rmse:1629596.125000 test-rmse:5677120.000000 [19] train-rmse:1628178.750000 test-rmse:5672229.500000 [20] train-rmse:1627209.250000 test-rmse:5672766.500000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:1879447.000000 test-rmse:4508587.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1519908.500000 test-rmse:4248733.500000 [3] train-rmse:1278278.375000 test-rmse:4027588.250000 [4] train-rmse:1112288.500000 test-rmse:3869586.000000 [5] train-rmse:994333.937500 test-rmse:3801905.250000 [6] train-rmse:922084.312500 test-rmse:3710865.750000 [7] train-rmse:869958.375000 test-rmse:3686040.750000 [8] train-rmse:833964.250000 test-rmse:3661386.000000 [9] train-rmse:809080.875000 test-rmse:3652012.750000 [10] train-rmse:789402.625000 test-rmse:3635615.500000 [11] train-rmse:776996.500000 test-rmse:3639627.000000 [12] train-rmse:766498.562500 test-rmse:3633216.250000 [13] train-rmse:757857.437500 test-rmse:3633350.750000 [14] train-rmse:752507.312500 test-rmse:3632966.250000 [15] train-rmse:746039.062500 test-rmse:3626616.250000 [16] train-rmse:743292.625000 test-rmse:3625980.500000 [17] train-rmse:738181.562500 test-rmse:3624873.250000 [18] train-rmse:735957.000000 test-rmse:3625730.500000 [19] train-rmse:732592.312500 test-rmse:3636964.250000 [20] train-rmse:730329.437500 test-rmse:3636366.500000 Stopping. Best iteration: [17] train-rmse:738181.562500 test-rmse:3624873.250000 [1] train-rmse:2446287.250000 test-rmse:2681877.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2074452.750000 test-rmse:2224638.000000 [3] train-rmse:1801913.375000 test-rmse:2147601.500000 [4] train-rmse:1591725.625000 test-rmse:2278975.250000 [5] train-rmse:1435080.250000 test-rmse:2525713.500000 [6] train-rmse:1310670.000000 test-rmse:2800561.250000 Stopping. Best iteration: [3] train-rmse:1801913.375000 test-rmse:2147601.500000 [1] train-rmse:2693620.000000 test-rmse:1621044.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2283690.000000 test-rmse:1320782.875000 [3] train-rmse:1986137.375000 test-rmse:1211494.375000 [4] train-rmse:1766086.125000 test-rmse:1113447.750000 [5] train-rmse:1604564.000000 test-rmse:1052446.125000 [6] train-rmse:1480253.250000 test-rmse:1012374.937500 [7] train-rmse:1392401.500000 test-rmse:1033635.000000 [8] train-rmse:1323861.500000 test-rmse:1031256.875000 [9] train-rmse:1276764.500000 test-rmse:1064139.250000 Stopping. Best iteration: [6] train-rmse:1480253.250000 test-rmse:1012374.937500 [1] train-rmse:2692269.250000 test-rmse:1674686.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2267063.000000 test-rmse:1485281.875000 [3] train-rmse:1958639.625000 test-rmse:1362056.125000 [4] train-rmse:1731132.500000 test-rmse:1270194.625000 [5] train-rmse:1566769.500000 test-rmse:1195876.375000 [6] train-rmse:1442600.875000 test-rmse:1154206.500000 [7] train-rmse:1351214.125000 test-rmse:1123806.375000 [8] train-rmse:1282963.375000 test-rmse:1109547.500000 [9] train-rmse:1234787.125000 test-rmse:1088004.125000 [10] train-rmse:1198863.125000 test-rmse:1079384.125000 [11] train-rmse:1170978.000000 test-rmse:1068255.625000 [12] train-rmse:1149624.375000 test-rmse:1064460.875000 [13] train-rmse:1133873.750000 test-rmse:1064503.000000 [14] train-rmse:1121325.000000 test-rmse:1064526.375000 [15] train-rmse:1111827.875000 test-rmse:1056000.375000 [16] train-rmse:1105782.000000 test-rmse:1057044.250000 [17] train-rmse:1097866.000000 test-rmse:1053972.500000 [18] train-rmse:1092753.375000 test-rmse:1052386.500000 [19] train-rmse:1087482.750000 test-rmse:1048009.937500 [20] train-rmse:1084049.125000 test-rmse:1043661.000000 [1] train-rmse:2754934.750000 test-rmse:1049443.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2317810.750000 test-rmse:1204002.125000 [3] train-rmse:2012078.625000 test-rmse:1436452.375000 [4] train-rmse:1782227.625000 test-rmse:1476197.750000 Stopping. Best iteration: [1] train-rmse:2754934.750000 test-rmse:1049443.375000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:3957569.250000 test-rmse:3813776.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3475568.000000 test-rmse:3615714.000000 [3] train-rmse:3165418.500000 test-rmse:3632355.250000 [4] train-rmse:2848835.750000 test-rmse:3712686.000000 [5] train-rmse:2625347.500000 test-rmse:3875362.250000 Stopping. Best iteration: [2] train-rmse:3475568.000000 test-rmse:3615714.000000 [1] train-rmse:3186154.000000 test-rmse:6832414.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2735878.500000 test-rmse:6203505.000000 [3] train-rmse:2396304.750000 test-rmse:5885531.500000 [4] train-rmse:2128031.500000 test-rmse:5645268.000000 [5] train-rmse:1918104.500000 test-rmse:5418400.500000 [6] train-rmse:1760304.250000 test-rmse:5246067.500000 [7] train-rmse:1630541.625000 test-rmse:5138442.000000 [8] train-rmse:1531900.375000 test-rmse:5031681.500000 [9] train-rmse:1457768.875000 test-rmse:4971213.000000 [10] train-rmse:1401989.000000 test-rmse:4905451.000000 [11] train-rmse:1360899.375000 test-rmse:4865804.500000 [12] train-rmse:1331922.125000 test-rmse:4814714.000000 [13] train-rmse:1307426.000000 test-rmse:4798028.500000 [14] train-rmse:1290473.125000 test-rmse:4786548.000000 [15] train-rmse:1277004.750000 test-rmse:4776128.500000 [16] train-rmse:1267506.875000 test-rmse:4764664.000000 [17] train-rmse:1260085.125000 test-rmse:4761824.000000 [18] train-rmse:1254608.000000 test-rmse:4758013.000000 [19] train-rmse:1251022.875000 test-rmse:4750219.500000 [20] train-rmse:1248430.875000 test-rmse:4739103.500000 [1] train-rmse:4368287.500000 test-rmse:242077.796875 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3757132.750000 test-rmse:203764.234375 [3] train-rmse:3368785.500000 test-rmse:182790.562500 [4] train-rmse:3015307.250000 test-rmse:170439.234375 [5] train-rmse:2762928.250000 test-rmse:164940.312500 [6] train-rmse:2583722.750000 test-rmse:161782.296875 [7] train-rmse:2456856.750000 test-rmse:160651.703125 [8] train-rmse:2365681.500000 test-rmse:159936.109375 [9] train-rmse:2298819.500000 test-rmse:159860.593750 [10] train-rmse:2250312.750000 test-rmse:159725.078125 [11] train-rmse:2217476.750000 test-rmse:159898.687500 [12] train-rmse:2192935.500000 test-rmse:160014.156250 [13] train-rmse:2175009.750000 test-rmse:159999.468750 Stopping. Best iteration: [10] train-rmse:2250312.750000 test-rmse:159725.078125 [1] train-rmse:4365078.000000 test-rmse:254282.468750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3754498.000000 test-rmse:255314.656250 [3] train-rmse:3366313.250000 test-rmse:349613.531250 [4] train-rmse:3032219.000000 test-rmse:459087.468750 Stopping. Best iteration: [1] train-rmse:4365078.000000 test-rmse:254282.468750 [1] train-rmse:3502114.250000 test-rmse:5943386.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3033674.750000 test-rmse:5857732.500000 [3] train-rmse:2682179.250000 test-rmse:5562565.000000 [4] train-rmse:2382255.500000 test-rmse:5523220.000000 [5] train-rmse:2179231.750000 test-rmse:5500615.000000 [6] train-rmse:2036575.750000 test-rmse:5478098.000000 [7] train-rmse:1942996.875000 test-rmse:5474077.000000 [8] train-rmse:1882033.625000 test-rmse:5463591.000000 [9] train-rmse:1839469.125000 test-rmse:5480048.000000 [10] train-rmse:1810783.000000 test-rmse:5515994.500000 [11] train-rmse:1793307.750000 test-rmse:5460877.500000 [12] train-rmse:1779486.125000 test-rmse:5415565.500000 [13] train-rmse:1771875.500000 test-rmse:5376053.000000 [14] train-rmse:1764302.250000 test-rmse:5345190.500000 [15] train-rmse:1758917.125000 test-rmse:5320062.000000 [16] train-rmse:1753871.750000 test-rmse:5346284.500000 [17] train-rmse:1750484.750000 test-rmse:5371285.000000 [18] train-rmse:1746972.500000 test-rmse:5356557.500000 Stopping. Best iteration: [15] train-rmse:1758917.125000 test-rmse:5320062.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:981683.437500 test-rmse:1563757.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:809818.375000 test-rmse:1587125.750000 [3] train-rmse:678065.937500 test-rmse:1643142.750000 [4] train-rmse:578361.312500 test-rmse:1708893.000000 Stopping. Best iteration: [1] train-rmse:981683.437500 test-rmse:1563757.500000 [1] train-rmse:1162764.375000 test-rmse:377265.812500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:950495.312500 test-rmse:417504.718750 [3] train-rmse:786767.437500 test-rmse:534337.187500 [4] train-rmse:661221.187500 test-rmse:654647.437500 Stopping. Best iteration: [1] train-rmse:1162764.375000 test-rmse:377265.812500 [1] train-rmse:1186593.125000 test-rmse:101784.679688 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:970110.187500 test-rmse:76386.539062 [3] train-rmse:802743.562500 test-rmse:59943.617188 [4] train-rmse:674728.687500 test-rmse:49687.714844 [5] train-rmse:577448.937500 test-rmse:43734.386719 [6] train-rmse:505631.500000 test-rmse:40603.671875 [7] train-rmse:453766.937500 test-rmse:38924.140625 [8] train-rmse:416982.281250 test-rmse:38039.343750 [9] train-rmse:391658.031250 test-rmse:37575.367188 [10] train-rmse:374354.562500 test-rmse:37330.718750 [11] train-rmse:362854.093750 test-rmse:37199.988281 [12] train-rmse:354875.312500 test-rmse:37140.128906 [13] train-rmse:349315.750000 test-rmse:37108.320312 [14] train-rmse:345532.531250 test-rmse:37091.050781 [15] train-rmse:342937.625000 test-rmse:37075.671875 [16] train-rmse:341655.937500 test-rmse:37066.183594 [17] train-rmse:340308.250000 test-rmse:37011.703125 [18] train-rmse:339384.875000 test-rmse:37035.519531 [19] train-rmse:338725.343750 test-rmse:37042.339844 [20] train-rmse:338263.812500 test-rmse:37052.531250 Stopping. Best iteration: [17] train-rmse:340308.250000 test-rmse:37011.703125 [1] train-rmse:1181605.125000 test-rmse:197411.656250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:965075.000000 test-rmse:166674.265625 [3] train-rmse:797733.437500 test-rmse:196620.421875 [4] train-rmse:669903.812500 test-rmse:242719.296875 [5] train-rmse:572312.750000 test-rmse:253330.171875 Stopping. Best iteration: [2] train-rmse:965075.000000 test-rmse:166674.265625 [1] train-rmse:688673.312500 test-rmse:2291018.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:552272.625000 test-rmse:2278091.250000 [3] train-rmse:445228.593750 test-rmse:2269898.500000 [4] train-rmse:359349.093750 test-rmse:2263911.500000 [5] train-rmse:291774.281250 test-rmse:2260558.750000 [6] train-rmse:238941.406250 test-rmse:2258341.750000 [7] train-rmse:197945.875000 test-rmse:2256873.000000 [8] train-rmse:166491.875000 test-rmse:2255683.750000 [9] train-rmse:142734.765625 test-rmse:2255071.250000 [10] train-rmse:125119.101562 test-rmse:2254720.250000 [11] train-rmse:112419.414062 test-rmse:2254477.750000 [12] train-rmse:102490.312500 test-rmse:2254464.000000 [13] train-rmse:94708.875000 test-rmse:2254490.750000 [14] train-rmse:88672.335938 test-rmse:2254488.750000 [15] train-rmse:84047.390625 test-rmse:2254546.750000 Stopping. Best iteration: [12] train-rmse:102490.312500 test-rmse:2254464.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:7817797.000000 test-rmse:3536962.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6804571.500000 test-rmse:3464171.000000 [3] train-rmse:6022244.000000 test-rmse:3419261.500000 [4] train-rmse:5388860.000000 test-rmse:3379279.500000 [5] train-rmse:4977097.000000 test-rmse:3374434.500000 [6] train-rmse:4669593.000000 test-rmse:3400423.250000 [7] train-rmse:4179075.000000 test-rmse:3414906.250000 [8] train-rmse:3959261.000000 test-rmse:3436597.000000 Stopping. Best iteration: [5] train-rmse:4977097.000000 test-rmse:3374434.500000 [1] train-rmse:7849339.500000 test-rmse:2949789.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6784648.000000 test-rmse:3066343.750000 [3] train-rmse:5928500.000000 test-rmse:3593534.000000 [4] train-rmse:5226660.000000 test-rmse:4266892.500000 Stopping. Best iteration: [1] train-rmse:7849339.500000 test-rmse:2949789.750000 [1] train-rmse:5554935.500000 test-rmse:12949010.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4910745.500000 test-rmse:14872465.000000 [3] train-rmse:4418246.500000 test-rmse:15462873.000000 [4] train-rmse:4042758.250000 test-rmse:16162203.000000 Stopping. Best iteration: [1] train-rmse:5554935.500000 test-rmse:12949010.000000 [1] train-rmse:7614501.000000 test-rmse:4655955.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6830340.000000 test-rmse:4642139.500000 [3] train-rmse:6223043.500000 test-rmse:4837615.000000 [4] train-rmse:5761670.500000 test-rmse:5448515.500000 [5] train-rmse:5018304.500000 test-rmse:5782105.000000 Stopping. Best iteration: [2] train-rmse:6830340.000000 test-rmse:4642139.500000 [1] train-rmse:6243516.500000 test-rmse:10464080.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5400383.000000 test-rmse:10434083.000000 [3] train-rmse:4738889.000000 test-rmse:10392898.000000 [4] train-rmse:4203502.000000 test-rmse:10399612.000000 [5] train-rmse:3767172.500000 test-rmse:10399185.000000 [6] train-rmse:3433101.000000 test-rmse:10402521.000000 Stopping. Best iteration: [3] train-rmse:4738889.000000 test-rmse:10392898.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:618077.125000 test-rmse:240689.703125 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:542501.250000 test-rmse:172092.125000 [3] train-rmse:498440.781250 test-rmse:136528.125000 [4] train-rmse:473491.218750 test-rmse:125563.531250 [5] train-rmse:459402.187500 test-rmse:124583.695312 [6] train-rmse:451368.562500 test-rmse:127355.648438 [7] train-rmse:446804.625000 test-rmse:132202.718750 [8] train-rmse:444157.781250 test-rmse:135981.609375 Stopping. Best iteration: [5] train-rmse:459402.187500 test-rmse:124583.695312 [1] train-rmse:546793.750000 test-rmse:695684.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:488410.500000 test-rmse:626426.375000 [3] train-rmse:455055.562500 test-rmse:584412.812500 [4] train-rmse:436603.093750 test-rmse:563392.062500 [5] train-rmse:426501.093750 test-rmse:553984.937500 [6] train-rmse:420885.468750 test-rmse:556830.562500 [7] train-rmse:417731.062500 test-rmse:561069.125000 [8] train-rmse:415966.062500 test-rmse:564595.750000 Stopping. Best iteration: [5] train-rmse:426501.093750 test-rmse:553984.937500 [1] train-rmse:443513.781250 test-rmse:940952.187500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:355252.375000 test-rmse:909926.875000 [3] train-rmse:298711.656250 test-rmse:891726.125000 [4] train-rmse:264357.843750 test-rmse:882042.625000 [5] train-rmse:243659.609375 test-rmse:876497.750000 [6] train-rmse:231423.593750 test-rmse:873258.625000 [7] train-rmse:224279.312500 test-rmse:871406.187500 [8] train-rmse:220116.265625 test-rmse:870082.625000 [9] train-rmse:217717.062500 test-rmse:869292.125000 [10] train-rmse:216208.312500 test-rmse:868936.875000 [11] train-rmse:215433.328125 test-rmse:868735.562500 [12] train-rmse:215017.859375 test-rmse:868523.375000 [13] train-rmse:214448.328125 test-rmse:868689.500000 [14] train-rmse:214148.984375 test-rmse:868845.187500 [15] train-rmse:213969.781250 test-rmse:868897.625000 Stopping. Best iteration: [12] train-rmse:215017.859375 test-rmse:868523.375000 [1] train-rmse:583964.937500 test-rmse:475766.187500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:517010.718750 test-rmse:376958.187500 [3] train-rmse:478069.500000 test-rmse:319718.531250 [4] train-rmse:455868.093750 test-rmse:298315.843750 [5] train-rmse:443304.500000 test-rmse:292310.125000 [6] train-rmse:436292.968750 test-rmse:293732.718750 [7] train-rmse:432318.812500 test-rmse:296212.125000 [8] train-rmse:430043.437500 test-rmse:299512.750000 Stopping. Best iteration: [5] train-rmse:443304.500000 test-rmse:292310.125000 [1] train-rmse:615926.062500 test-rmse:282818.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:541834.937500 test-rmse:213540.250000 [3] train-rmse:498796.531250 test-rmse:168982.078125 [4] train-rmse:474387.937500 test-rmse:141689.421875 [5] train-rmse:460628.718750 test-rmse:125528.007812 [6] train-rmse:452763.718750 test-rmse:118359.640625 [7] train-rmse:448291.656250 test-rmse:114217.859375 [8] train-rmse:445727.156250 test-rmse:112535.664062 [9] train-rmse:444331.406250 test-rmse:111835.960938 [10] train-rmse:443391.125000 test-rmse:111509.656250 [11] train-rmse:442796.375000 test-rmse:111474.718750 [12] train-rmse:442355.812500 test-rmse:111504.312500 [13] train-rmse:442076.156250 test-rmse:111430.617188 [14] train-rmse:441858.625000 test-rmse:111530.750000 [15] train-rmse:441758.031250 test-rmse:111318.390625 [16] train-rmse:441667.531250 test-rmse:111257.562500 [17] train-rmse:441621.593750 test-rmse:111207.789062 [18] train-rmse:441555.500000 test-rmse:111262.226562 [19] train-rmse:441542.625000 test-rmse:111265.140625 [20] train-rmse:441437.406250 test-rmse:110987.382812
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:381680.093750 test-rmse:366662.468750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:295880.406250 test-rmse:286085.906250 [3] train-rmse:242027.203125 test-rmse:233891.187500 [4] train-rmse:210054.578125 test-rmse:203334.468750 [5] train-rmse:191574.156250 test-rmse:187284.453125 [6] train-rmse:181820.968750 test-rmse:179297.718750 [7] train-rmse:176319.375000 test-rmse:175873.390625 [8] train-rmse:173681.250000 test-rmse:174808.796875 [9] train-rmse:172073.390625 test-rmse:174076.078125 [10] train-rmse:171260.078125 test-rmse:174209.062500 [11] train-rmse:170659.656250 test-rmse:174332.234375 [12] train-rmse:170374.937500 test-rmse:174238.515625 Stopping. Best iteration: [9] train-rmse:172073.390625 test-rmse:174076.078125 [1] train-rmse:373174.062500 test-rmse:393011.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:286653.937500 test-rmse:312340.406250 [3] train-rmse:232014.921875 test-rmse:267646.468750 [4] train-rmse:199302.390625 test-rmse:246882.562500 [5] train-rmse:180794.531250 test-rmse:238633.453125 [6] train-rmse:170610.609375 test-rmse:237793.640625 [7] train-rmse:165281.546875 test-rmse:239613.343750 [8] train-rmse:162512.578125 test-rmse:242184.546875 [9] train-rmse:161047.421875 test-rmse:244825.984375 Stopping. Best iteration: [6] train-rmse:170610.609375 test-rmse:237793.640625 [1] train-rmse:370419.250000 test-rmse:424952.468750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:288229.593750 test-rmse:336678.250000 [3] train-rmse:237255.000000 test-rmse:283332.218750 [4] train-rmse:206767.593750 test-rmse:249280.921875 [5] train-rmse:189970.687500 test-rmse:229142.968750 [6] train-rmse:180917.328125 test-rmse:217301.562500 [7] train-rmse:176094.968750 test-rmse:210125.843750 [8] train-rmse:173428.421875 test-rmse:204850.156250 [9] train-rmse:172102.406250 test-rmse:201588.171875 [10] train-rmse:171267.093750 test-rmse:199717.234375 [11] train-rmse:170787.921875 test-rmse:198096.250000 [12] train-rmse:170522.265625 test-rmse:197266.656250 [13] train-rmse:170317.343750 test-rmse:196858.593750 [14] train-rmse:170224.718750 test-rmse:196554.593750 [15] train-rmse:170112.453125 test-rmse:196816.000000 [16] train-rmse:170003.000000 test-rmse:196975.203125 [17] train-rmse:169898.421875 test-rmse:197209.609375 Stopping. Best iteration: [14] train-rmse:170224.718750 test-rmse:196554.593750 [1] train-rmse:383190.031250 test-rmse:347271.937500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:297692.937500 test-rmse:256651.656250 [3] train-rmse:244079.843750 test-rmse:204854.671875 [4] train-rmse:212265.437500 test-rmse:179332.390625 [5] train-rmse:194027.031250 test-rmse:165827.328125 [6] train-rmse:184104.562500 test-rmse:160272.765625 [7] train-rmse:178880.109375 test-rmse:159807.187500 [8] train-rmse:175962.390625 test-rmse:160508.546875 [9] train-rmse:174535.406250 test-rmse:161680.218750 [10] train-rmse:172898.078125 test-rmse:163270.171875 Stopping. Best iteration: [7] train-rmse:178880.109375 test-rmse:159807.187500 [1] train-rmse:385546.468750 test-rmse:339470.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:299045.781250 test-rmse:260301.000000 [3] train-rmse:244938.312500 test-rmse:209969.390625 [4] train-rmse:212715.578125 test-rmse:182690.546875 [5] train-rmse:194783.359375 test-rmse:169436.171875 [6] train-rmse:185065.328125 test-rmse:165918.109375 [7] train-rmse:179086.718750 test-rmse:164774.093750 [8] train-rmse:176160.750000 test-rmse:165589.203125 [9] train-rmse:174649.250000 test-rmse:168273.375000 [10] train-rmse:173729.406250 test-rmse:168754.031250 Stopping. Best iteration: [7] train-rmse:179086.718750 test-rmse:164774.093750
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:2190132.000000 test-rmse:1575229.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1975286.250000 test-rmse:1319403.125000 [3] train-rmse:1839436.125000 test-rmse:1188508.625000 [4] train-rmse:1733642.000000 test-rmse:1131386.375000 [5] train-rmse:1678510.375000 test-rmse:1086495.875000 [6] train-rmse:1635338.625000 test-rmse:1089178.000000 [7] train-rmse:1596151.750000 test-rmse:1119782.250000 [8] train-rmse:1569620.000000 test-rmse:1151212.250000 Stopping. Best iteration: [5] train-rmse:1678510.375000 test-rmse:1086495.875000 [1] train-rmse:2146260.250000 test-rmse:1886774.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1930101.625000 test-rmse:1701305.250000 [3] train-rmse:1791096.125000 test-rmse:1590748.625000 [4] train-rmse:1703774.625000 test-rmse:1563222.875000 [5] train-rmse:1652199.500000 test-rmse:1526190.875000 [6] train-rmse:1611507.750000 test-rmse:1515414.125000 [7] train-rmse:1589260.125000 test-rmse:1512245.000000 [8] train-rmse:1558279.000000 test-rmse:1514624.000000 [9] train-rmse:1543950.625000 test-rmse:1517764.375000 [10] train-rmse:1526556.000000 test-rmse:1513766.875000 Stopping. Best iteration: [7] train-rmse:1589260.125000 test-rmse:1512245.000000 [1] train-rmse:2217087.000000 test-rmse:1260384.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1980997.375000 test-rmse:1066374.500000 [3] train-rmse:1832480.250000 test-rmse:1032161.937500 [4] train-rmse:1744598.500000 test-rmse:1042642.437500 [5] train-rmse:1683871.750000 test-rmse:1119435.750000 [6] train-rmse:1638606.375000 test-rmse:1222416.750000 Stopping. Best iteration: [3] train-rmse:1832480.250000 test-rmse:1032161.937500 [1] train-rmse:2141323.500000 test-rmse:1823651.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1925158.625000 test-rmse:1657447.125000 [3] train-rmse:1791964.500000 test-rmse:1551746.500000 [4] train-rmse:1708540.750000 test-rmse:1496313.375000 [5] train-rmse:1648423.625000 test-rmse:1466450.125000 [6] train-rmse:1607739.500000 test-rmse:1469827.375000 [7] train-rmse:1583485.750000 test-rmse:1460554.375000 [8] train-rmse:1562473.000000 test-rmse:1450606.375000 [9] train-rmse:1535979.250000 test-rmse:1451631.750000 [10] train-rmse:1521283.500000 test-rmse:1448412.000000 [11] train-rmse:1511368.875000 test-rmse:1447255.625000 [12] train-rmse:1502028.625000 test-rmse:1436370.375000 [13] train-rmse:1497187.500000 test-rmse:1434789.875000 [14] train-rmse:1493604.250000 test-rmse:1436938.250000 [15] train-rmse:1485712.625000 test-rmse:1440402.750000 [16] train-rmse:1479547.375000 test-rmse:1445535.625000 Stopping. Best iteration: [13] train-rmse:1497187.500000 test-rmse:1434789.875000 [1] train-rmse:1643461.500000 test-rmse:3444000.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1378590.500000 test-rmse:3349549.000000 [3] train-rmse:1208382.750000 test-rmse:3288868.000000 [4] train-rmse:1081845.625000 test-rmse:3242314.250000 [5] train-rmse:1010132.375000 test-rmse:3217980.500000 [6] train-rmse:953691.250000 test-rmse:3194234.250000 [7] train-rmse:913415.125000 test-rmse:3174276.500000 [8] train-rmse:885270.750000 test-rmse:3164915.250000 [9] train-rmse:857435.500000 test-rmse:3156177.500000 [10] train-rmse:838499.687500 test-rmse:3156868.750000 [11] train-rmse:824629.625000 test-rmse:3152833.750000 [12] train-rmse:808833.187500 test-rmse:3147642.000000 [13] train-rmse:796413.187500 test-rmse:3144253.250000 [14] train-rmse:784611.000000 test-rmse:3146344.500000 [15] train-rmse:776753.562500 test-rmse:3147304.750000 [16] train-rmse:771092.562500 test-rmse:3150458.000000 Stopping. Best iteration: [13] train-rmse:796413.187500 test-rmse:3144253.250000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:3357434.500000 test-rmse:1752655.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2784219.750000 test-rmse:1690425.500000 [3] train-rmse:2377457.750000 test-rmse:1649112.375000 [4] train-rmse:2049933.125000 test-rmse:1626279.625000 [5] train-rmse:1789711.625000 test-rmse:1559282.000000 [6] train-rmse:1611617.500000 test-rmse:1554552.000000 [7] train-rmse:1464206.875000 test-rmse:1489457.625000 [8] train-rmse:1353487.125000 test-rmse:1326832.375000 [9] train-rmse:1270613.375000 test-rmse:1314210.750000 [10] train-rmse:1206285.750000 test-rmse:1314845.375000 [11] train-rmse:1156091.250000 test-rmse:1324212.250000 [12] train-rmse:1120081.625000 test-rmse:1330268.125000 Stopping. Best iteration: [9] train-rmse:1270613.375000 test-rmse:1314210.750000 [1] train-rmse:3038443.250000 test-rmse:3573433.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2521693.500000 test-rmse:3386903.250000 [3] train-rmse:2134512.250000 test-rmse:3204300.250000 [4] train-rmse:1867105.250000 test-rmse:3126458.500000 [5] train-rmse:1635643.000000 test-rmse:3141509.000000 [6] train-rmse:1478850.250000 test-rmse:3204867.750000 [7] train-rmse:1344439.625000 test-rmse:3300726.250000 Stopping. Best iteration: [4] train-rmse:1867105.250000 test-rmse:3126458.500000 [1] train-rmse:3165279.750000 test-rmse:2718278.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2606020.000000 test-rmse:2450618.750000 [3] train-rmse:2160556.500000 test-rmse:2279955.250000 [4] train-rmse:1862249.250000 test-rmse:2153919.250000 [5] train-rmse:1576210.250000 test-rmse:2112562.250000 [6] train-rmse:1349721.000000 test-rmse:2092272.875000 [7] train-rmse:1173138.375000 test-rmse:2084024.125000 [8] train-rmse:1031613.125000 test-rmse:2077504.875000 [9] train-rmse:920882.625000 test-rmse:2073822.000000 [10] train-rmse:832139.125000 test-rmse:2076714.000000 [11] train-rmse:762914.000000 test-rmse:2076868.000000 [12] train-rmse:708610.375000 test-rmse:2094976.375000 Stopping. Best iteration: [9] train-rmse:920882.625000 test-rmse:2073822.000000 [1] train-rmse:3410896.750000 test-rmse:981653.687500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2883528.750000 test-rmse:650180.000000 [3] train-rmse:2428703.500000 test-rmse:485718.625000 [4] train-rmse:2105257.500000 test-rmse:410321.343750 [5] train-rmse:1864487.000000 test-rmse:402000.937500 [6] train-rmse:1661028.500000 test-rmse:447617.218750 [7] train-rmse:1503398.625000 test-rmse:522561.843750 [8] train-rmse:1389392.000000 test-rmse:594796.250000 Stopping. Best iteration: [5] train-rmse:1864487.000000 test-rmse:402000.937500 [1] train-rmse:2341154.750000 test-rmse:6059164.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1992592.750000 test-rmse:5689809.500000 [3] train-rmse:1706511.875000 test-rmse:5559709.500000 [4] train-rmse:1501858.625000 test-rmse:5425432.000000 [5] train-rmse:1357803.750000 test-rmse:5374599.000000 [6] train-rmse:1257038.250000 test-rmse:5275302.000000 [7] train-rmse:1182695.125000 test-rmse:5210414.000000 [8] train-rmse:1120650.250000 test-rmse:5204221.500000 [9] train-rmse:1076616.875000 test-rmse:5195237.000000 [10] train-rmse:1039639.312500 test-rmse:5209161.000000 [11] train-rmse:1016060.250000 test-rmse:5201335.500000 [12] train-rmse:996507.875000 test-rmse:5202937.000000 Stopping. Best iteration: [9] train-rmse:1076616.875000 test-rmse:5195237.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:3073189.000000 test-rmse:2089022.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2751188.250000 test-rmse:2016791.750000 [3] train-rmse:2509056.000000 test-rmse:1983912.125000 [4] train-rmse:2328369.500000 test-rmse:1967785.375000 [5] train-rmse:2193702.750000 test-rmse:1958780.375000 [6] train-rmse:2092127.625000 test-rmse:1953797.125000 [7] train-rmse:2016937.125000 test-rmse:1951975.000000 [8] train-rmse:1961698.750000 test-rmse:1951524.000000 [9] train-rmse:1921325.500000 test-rmse:1951308.875000 [10] train-rmse:1891296.875000 test-rmse:1948667.625000 [11] train-rmse:1868821.500000 test-rmse:1947980.875000 [12] train-rmse:1852730.500000 test-rmse:1946299.750000 [13] train-rmse:1841185.375000 test-rmse:1946407.875000 [14] train-rmse:1832299.000000 test-rmse:1947018.000000 [15] train-rmse:1826227.625000 test-rmse:1947249.000000 Stopping. Best iteration: [12] train-rmse:1852730.500000 test-rmse:1946299.750000 [1] train-rmse:3143255.000000 test-rmse:1609133.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2842562.000000 test-rmse:1415821.375000 [3] train-rmse:2617662.000000 test-rmse:1315486.125000 [4] train-rmse:2416640.750000 test-rmse:1262876.250000 [5] train-rmse:2287911.250000 test-rmse:1244296.500000 [6] train-rmse:2184941.250000 test-rmse:1237917.000000 [7] train-rmse:2070252.750000 test-rmse:1235555.750000 [8] train-rmse:1984175.750000 test-rmse:1236587.125000 [9] train-rmse:1919571.125000 test-rmse:1241630.375000 [10] train-rmse:1887164.750000 test-rmse:1245102.125000 Stopping. Best iteration: [7] train-rmse:2070252.750000 test-rmse:1235555.750000 [1] train-rmse:1460093.125000 test-rmse:6457740.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1273689.125000 test-rmse:6428283.000000 [3] train-rmse:1161149.625000 test-rmse:6415954.000000 [4] train-rmse:1117297.250000 test-rmse:6404084.000000 [5] train-rmse:1093554.500000 test-rmse:6397969.000000 [6] train-rmse:1079748.125000 test-rmse:6398165.000000 [7] train-rmse:1073071.000000 test-rmse:6396467.500000 [8] train-rmse:1043248.187500 test-rmse:6395328.000000 [9] train-rmse:968881.187500 test-rmse:6395520.000000 [10] train-rmse:966676.875000 test-rmse:6394779.000000 [11] train-rmse:909231.000000 test-rmse:6394308.500000 [12] train-rmse:864447.312500 test-rmse:6392817.000000 [13] train-rmse:829324.375000 test-rmse:6392840.500000 [14] train-rmse:803493.437500 test-rmse:6392874.000000 [15] train-rmse:785429.500000 test-rmse:6392995.000000 Stopping. Best iteration: [12] train-rmse:864447.312500 test-rmse:6392817.000000 [1] train-rmse:3212069.500000 test-rmse:957996.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2902555.250000 test-rmse:837202.875000 [3] train-rmse:2673274.500000 test-rmse:770689.312500 [4] train-rmse:2503765.500000 test-rmse:730030.687500 [5] train-rmse:2378834.750000 test-rmse:704952.937500 [6] train-rmse:2252987.000000 test-rmse:690379.312500 [7] train-rmse:2183233.500000 test-rmse:678413.062500 [8] train-rmse:2129817.750000 test-rmse:658921.750000 [9] train-rmse:2047636.750000 test-rmse:654422.812500 [10] train-rmse:1986002.125000 test-rmse:651760.187500 [11] train-rmse:1952149.125000 test-rmse:633812.500000 [12] train-rmse:1915191.250000 test-rmse:628437.687500 [13] train-rmse:1888133.125000 test-rmse:628115.375000 [14] train-rmse:1867940.750000 test-rmse:630943.437500 [15] train-rmse:1853493.500000 test-rmse:634521.812500 [16] train-rmse:1842876.250000 test-rmse:636498.250000 Stopping. Best iteration: [13] train-rmse:1888133.125000 test-rmse:628115.375000 [1] train-rmse:3207664.250000 test-rmse:1020703.187500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2861219.250000 test-rmse:953904.000000 [3] train-rmse:2624122.250000 test-rmse:889660.562500 [4] train-rmse:2450031.250000 test-rmse:851079.812500 [5] train-rmse:2287312.500000 test-rmse:932952.750000 [6] train-rmse:2187653.500000 test-rmse:922814.062500 [7] train-rmse:2089848.250000 test-rmse:1044757.187500 Stopping. Best iteration: [4] train-rmse:2450031.250000 test-rmse:851079.812500
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:14326104.000000 test-rmse:3971924.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:12260562.000000 test-rmse:3663572.750000 [3] train-rmse:10679911.000000 test-rmse:3536341.500000 [4] train-rmse:9488357.000000 test-rmse:3587648.750000 [5] train-rmse:8586038.000000 test-rmse:3684673.500000 [6] train-rmse:7921149.500000 test-rmse:3802383.250000 Stopping. Best iteration: [3] train-rmse:10679911.000000 test-rmse:3536341.500000 [1] train-rmse:14127263.000000 test-rmse:6240130.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:12078039.000000 test-rmse:5772019.000000 [3] train-rmse:10522072.000000 test-rmse:5697583.500000 [4] train-rmse:9366390.000000 test-rmse:5780395.500000 [5] train-rmse:8492793.000000 test-rmse:5919072.000000 [6] train-rmse:7836780.500000 test-rmse:6050043.500000 Stopping. Best iteration: [3] train-rmse:10522072.000000 test-rmse:5697583.500000 [1] train-rmse:7720590.500000 test-rmse:28874106.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6741381.500000 test-rmse:28683468.000000 [3] train-rmse:6021085.500000 test-rmse:28594492.000000 [4] train-rmse:5484389.000000 test-rmse:28562918.000000 [5] train-rmse:5093294.000000 test-rmse:28540254.000000 [6] train-rmse:4787644.500000 test-rmse:28517910.000000 [7] train-rmse:4579112.500000 test-rmse:28507750.000000 [8] train-rmse:4399987.500000 test-rmse:28484040.000000 [9] train-rmse:4288543.000000 test-rmse:28467528.000000 [10] train-rmse:4191774.250000 test-rmse:28463578.000000 [11] train-rmse:4108624.750000 test-rmse:28457744.000000 [12] train-rmse:4072475.250000 test-rmse:28451580.000000 [13] train-rmse:4023673.500000 test-rmse:28445674.000000 [14] train-rmse:3959909.250000 test-rmse:28446220.000000 [15] train-rmse:3884625.500000 test-rmse:28444142.000000 [16] train-rmse:3869952.250000 test-rmse:28432300.000000 [17] train-rmse:3783664.500000 test-rmse:28425450.000000 [18] train-rmse:3776214.000000 test-rmse:28420486.000000 [19] train-rmse:3768624.000000 test-rmse:28412254.000000 [20] train-rmse:3724886.000000 test-rmse:28381510.000000 [1] train-rmse:14212922.000000 test-rmse:5412808.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:12173036.000000 test-rmse:4806676.000000 [3] train-rmse:10620518.000000 test-rmse:4278499.500000 [4] train-rmse:9441194.000000 test-rmse:3954190.500000 [5] train-rmse:8560386.000000 test-rmse:3830915.250000 [6] train-rmse:7897899.500000 test-rmse:3760297.250000 [7] train-rmse:7400973.500000 test-rmse:3742145.000000 [8] train-rmse:7018741.500000 test-rmse:3744300.250000 [9] train-rmse:6721701.000000 test-rmse:3802987.250000 [10] train-rmse:6520765.500000 test-rmse:3819902.750000 Stopping. Best iteration: [7] train-rmse:7400973.500000 test-rmse:3742145.000000 [1] train-rmse:13005268.000000 test-rmse:14083286.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:11045386.000000 test-rmse:13902020.000000 [3] train-rmse:9591645.000000 test-rmse:13764552.000000 [4] train-rmse:8512878.000000 test-rmse:13681201.000000 [5] train-rmse:7687896.000000 test-rmse:13603895.000000 [6] train-rmse:7077619.500000 test-rmse:13585750.000000 [7] train-rmse:6631616.000000 test-rmse:13573223.000000 [8] train-rmse:6304869.000000 test-rmse:13576007.000000 [9] train-rmse:6065200.000000 test-rmse:13566142.000000 [10] train-rmse:5881609.500000 test-rmse:13585691.000000 [11] train-rmse:5734397.000000 test-rmse:13562893.000000 [12] train-rmse:5632591.000000 test-rmse:13553649.000000 [13] train-rmse:5551203.500000 test-rmse:13577086.000000 [14] train-rmse:5498133.000000 test-rmse:13576207.000000 [15] train-rmse:5452932.000000 test-rmse:13595755.000000 Stopping. Best iteration: [12] train-rmse:5632591.000000 test-rmse:13553649.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:958022.062500 test-rmse:1265520.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:788586.312500 test-rmse:1079757.500000 [3] train-rmse:681382.000000 test-rmse:950282.250000 [4] train-rmse:614713.750000 test-rmse:865693.875000 [5] train-rmse:574185.312500 test-rmse:828464.812500 [6] train-rmse:548493.187500 test-rmse:797360.812500 [7] train-rmse:533798.875000 test-rmse:780683.687500 [8] train-rmse:525392.937500 test-rmse:776991.062500 [9] train-rmse:520698.218750 test-rmse:781586.312500 [10] train-rmse:515125.875000 test-rmse:773997.312500 [11] train-rmse:509396.000000 test-rmse:775668.375000 [12] train-rmse:507072.218750 test-rmse:789248.125000 [13] train-rmse:505765.718750 test-rmse:795586.812500 Stopping. Best iteration: [10] train-rmse:515125.875000 test-rmse:773997.312500 [1] train-rmse:1047288.937500 test-rmse:842300.812500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:853277.562500 test-rmse:703019.187500 [3] train-rmse:727751.687500 test-rmse:625169.375000 [4] train-rmse:650100.125000 test-rmse:610666.250000 [5] train-rmse:604164.312500 test-rmse:616096.625000 [6] train-rmse:577017.187500 test-rmse:618832.375000 [7] train-rmse:553354.187500 test-rmse:620836.000000 Stopping. Best iteration: [4] train-rmse:650100.125000 test-rmse:610666.250000 [1] train-rmse:1076255.375000 test-rmse:627927.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:883149.312500 test-rmse:503818.343750 [3] train-rmse:760099.625000 test-rmse:438389.750000 [4] train-rmse:673586.937500 test-rmse:416919.687500 [5] train-rmse:628613.562500 test-rmse:427955.312500 [6] train-rmse:595606.875000 test-rmse:444314.250000 [7] train-rmse:578183.812500 test-rmse:457147.812500 Stopping. Best iteration: [4] train-rmse:673586.937500 test-rmse:416919.687500 [1] train-rmse:1048385.250000 test-rmse:876159.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:858867.500000 test-rmse:739301.375000 [3] train-rmse:738717.812500 test-rmse:663768.000000 [4] train-rmse:660829.125000 test-rmse:615606.000000 [5] train-rmse:617018.250000 test-rmse:583565.062500 [6] train-rmse:590998.125000 test-rmse:561488.187500 [7] train-rmse:564247.687500 test-rmse:553559.062500 [8] train-rmse:551853.437500 test-rmse:551148.937500 [9] train-rmse:545465.375000 test-rmse:527324.812500 [10] train-rmse:540305.625000 test-rmse:529223.937500 [11] train-rmse:536243.000000 test-rmse:525686.750000 [12] train-rmse:529298.312500 test-rmse:524972.062500 [13] train-rmse:524478.187500 test-rmse:525802.437500 [14] train-rmse:519151.250000 test-rmse:515255.843750 [15] train-rmse:517825.625000 test-rmse:507310.875000 [16] train-rmse:509982.375000 test-rmse:509162.687500 [17] train-rmse:505488.593750 test-rmse:510578.343750 [18] train-rmse:502493.593750 test-rmse:511940.468750 Stopping. Best iteration: [15] train-rmse:517825.625000 test-rmse:507310.875000 [1] train-rmse:914531.000000 test-rmse:1421658.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:736077.000000 test-rmse:1311906.875000 [3] train-rmse:623235.937500 test-rmse:1239632.750000 [4] train-rmse:551148.312500 test-rmse:1219087.000000 [5] train-rmse:503110.093750 test-rmse:1193179.125000 [6] train-rmse:477859.406250 test-rmse:1181282.625000 [7] train-rmse:456864.656250 test-rmse:1187151.625000 [8] train-rmse:445477.687500 test-rmse:1186672.750000 [9] train-rmse:437749.437500 test-rmse:1197556.500000 Stopping. Best iteration: [6] train-rmse:477859.406250 test-rmse:1181282.625000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:897049.000000 test-rmse:991457.812500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:718003.375000 test-rmse:796099.187500 [3] train-rmse:607295.937500 test-rmse:697498.937500 [4] train-rmse:540262.375000 test-rmse:645089.125000 [5] train-rmse:501753.437500 test-rmse:615789.250000 [6] train-rmse:481209.500000 test-rmse:611623.437500 [7] train-rmse:470245.500000 test-rmse:607780.437500 [8] train-rmse:463756.062500 test-rmse:616188.875000 [9] train-rmse:457513.562500 test-rmse:619106.625000 [10] train-rmse:452719.437500 test-rmse:620528.750000 Stopping. Best iteration: [7] train-rmse:470245.500000 test-rmse:607780.437500 [1] train-rmse:884589.375000 test-rmse:1092470.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:704498.750000 test-rmse:953037.812500 [3] train-rmse:593668.750000 test-rmse:866095.500000 [4] train-rmse:529298.187500 test-rmse:821570.750000 [5] train-rmse:492083.031250 test-rmse:788371.750000 [6] train-rmse:472019.937500 test-rmse:775712.062500 [7] train-rmse:460807.687500 test-rmse:763887.000000 [8] train-rmse:454874.625000 test-rmse:757269.312500 [9] train-rmse:449182.500000 test-rmse:753686.187500 [10] train-rmse:446027.687500 test-rmse:746497.000000 [11] train-rmse:444317.875000 test-rmse:730867.312500 [12] train-rmse:441128.656250 test-rmse:722280.000000 [13] train-rmse:440298.468750 test-rmse:715677.062500 [14] train-rmse:439322.156250 test-rmse:712029.750000 [15] train-rmse:438138.500000 test-rmse:708977.687500 [16] train-rmse:437643.781250 test-rmse:707773.562500 [17] train-rmse:435975.093750 test-rmse:707730.812500 [18] train-rmse:435742.281250 test-rmse:704768.437500 [19] train-rmse:434528.937500 test-rmse:703328.000000 [20] train-rmse:434428.562500 test-rmse:701625.437500 [1] train-rmse:993978.625000 test-rmse:444880.031250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:801891.812500 test-rmse:340134.125000 [3] train-rmse:678494.562500 test-rmse:351381.687500 [4] train-rmse:600719.062500 test-rmse:388960.593750 [5] train-rmse:558989.687500 test-rmse:419036.687500 Stopping. Best iteration: [2] train-rmse:801891.812500 test-rmse:340134.125000 [1] train-rmse:946736.312500 test-rmse:762479.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:757708.500000 test-rmse:622679.562500 [3] train-rmse:638117.062500 test-rmse:536098.750000 [4] train-rmse:566846.750000 test-rmse:492612.312500 [5] train-rmse:525889.062500 test-rmse:468584.125000 [6] train-rmse:503375.812500 test-rmse:459912.812500 [7] train-rmse:490251.625000 test-rmse:451871.156250 [8] train-rmse:482076.593750 test-rmse:450903.937500 [9] train-rmse:477458.562500 test-rmse:449962.625000 [10] train-rmse:473427.000000 test-rmse:452764.375000 [11] train-rmse:471010.375000 test-rmse:453467.781250 [12] train-rmse:469526.406250 test-rmse:455313.843750 Stopping. Best iteration: [9] train-rmse:477458.562500 test-rmse:449962.625000 [1] train-rmse:854172.375000 test-rmse:1217775.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:687692.000000 test-rmse:1046696.062500 [3] train-rmse:584426.750000 test-rmse:946487.875000 [4] train-rmse:518935.562500 test-rmse:848310.250000 [5] train-rmse:480024.562500 test-rmse:790208.437500 [6] train-rmse:459303.812500 test-rmse:756590.000000 [7] train-rmse:446400.062500 test-rmse:738650.562500 [8] train-rmse:439662.125000 test-rmse:725298.000000 [9] train-rmse:435821.218750 test-rmse:721657.562500 [10] train-rmse:432324.062500 test-rmse:715895.875000 [11] train-rmse:430733.625000 test-rmse:714086.125000 [12] train-rmse:429277.718750 test-rmse:706331.125000 [13] train-rmse:428036.906250 test-rmse:700230.750000 [14] train-rmse:426808.312500 test-rmse:677540.562500 [15] train-rmse:425339.375000 test-rmse:655476.875000 [16] train-rmse:423982.500000 test-rmse:656707.875000 [17] train-rmse:423593.687500 test-rmse:650026.375000 [18] train-rmse:422837.625000 test-rmse:651674.062500 [19] train-rmse:422644.750000 test-rmse:649112.875000 [20] train-rmse:422276.625000 test-rmse:644957.125000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:1329938.875000 test-rmse:2258990.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1119670.000000 test-rmse:1952489.875000 [3] train-rmse:975005.750000 test-rmse:1702960.375000 [4] train-rmse:879509.562500 test-rmse:1531079.500000 [5] train-rmse:819652.187500 test-rmse:1423289.125000 [6] train-rmse:779556.875000 test-rmse:1334545.250000 [7] train-rmse:750230.125000 test-rmse:1284509.625000 [8] train-rmse:732778.937500 test-rmse:1242930.375000 [9] train-rmse:720177.375000 test-rmse:1221563.750000 [10] train-rmse:712703.500000 test-rmse:1205020.125000 [11] train-rmse:707950.500000 test-rmse:1191574.625000 [12] train-rmse:704732.500000 test-rmse:1181482.125000 [13] train-rmse:702601.687500 test-rmse:1174870.875000 [14] train-rmse:700146.687500 test-rmse:1175826.375000 [15] train-rmse:694588.437500 test-rmse:1170398.000000 [16] train-rmse:691408.437500 test-rmse:1169233.750000 [17] train-rmse:690388.437500 test-rmse:1166519.625000 [18] train-rmse:689438.437500 test-rmse:1165307.000000 [19] train-rmse:688776.125000 test-rmse:1162890.500000 [20] train-rmse:687102.875000 test-rmse:1160008.875000 [1] train-rmse:1581198.125000 test-rmse:1443205.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1332322.000000 test-rmse:1321302.250000 [3] train-rmse:1167846.125000 test-rmse:1272076.625000 [4] train-rmse:1065003.375000 test-rmse:1205512.750000 [5] train-rmse:991594.250000 test-rmse:1204287.750000 [6] train-rmse:940484.812500 test-rmse:1214342.875000 [7] train-rmse:907553.375000 test-rmse:1235361.250000 [8] train-rmse:883832.875000 test-rmse:1256217.750000 Stopping. Best iteration: [5] train-rmse:991594.250000 test-rmse:1204287.750000 [1] train-rmse:1666260.625000 test-rmse:725840.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1396744.250000 test-rmse:718249.187500 [3] train-rmse:1228804.500000 test-rmse:732061.125000 [4] train-rmse:1107883.875000 test-rmse:703620.000000 [5] train-rmse:1031737.312500 test-rmse:702322.000000 [6] train-rmse:981161.875000 test-rmse:702615.812500 [7] train-rmse:945559.812500 test-rmse:704486.250000 [8] train-rmse:918265.375000 test-rmse:718450.250000 Stopping. Best iteration: [5] train-rmse:1031737.312500 test-rmse:702322.000000 [1] train-rmse:1530750.375000 test-rmse:1487918.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1286591.750000 test-rmse:1341976.000000 [3] train-rmse:1116630.625000 test-rmse:1274233.000000 [4] train-rmse:1016263.375000 test-rmse:1261036.000000 [5] train-rmse:955878.000000 test-rmse:1244322.500000 [6] train-rmse:913928.187500 test-rmse:1257548.625000 [7] train-rmse:875095.812500 test-rmse:1294521.625000 [8] train-rmse:855850.937500 test-rmse:1319598.000000 Stopping. Best iteration: [5] train-rmse:955878.000000 test-rmse:1244322.500000 [1] train-rmse:1433031.625000 test-rmse:1850142.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1158111.875000 test-rmse:1746276.750000 [3] train-rmse:970603.812500 test-rmse:1675737.000000 [4] train-rmse:840963.187500 test-rmse:1656003.875000 [5] train-rmse:752474.062500 test-rmse:1662484.000000 [6] train-rmse:693689.750000 test-rmse:1679929.250000 [7] train-rmse:653860.375000 test-rmse:1693688.875000 Stopping. Best iteration: [4] train-rmse:840963.187500 test-rmse:1656003.875000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:1962014.875000 test-rmse:4763253.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1803956.250000 test-rmse:4593502.500000 [3] train-rmse:1714571.375000 test-rmse:4491691.000000 [4] train-rmse:1654562.875000 test-rmse:4496856.000000 [5] train-rmse:1618062.125000 test-rmse:4505612.000000 [6] train-rmse:1598437.375000 test-rmse:4497724.500000 Stopping. Best iteration: [3] train-rmse:1714571.375000 test-rmse:4491691.000000 [1] train-rmse:2783784.250000 test-rmse:1840767.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2475760.000000 test-rmse:2057946.250000 [3] train-rmse:2238617.250000 test-rmse:2425903.250000 [4] train-rmse:2061786.000000 test-rmse:2922986.250000 Stopping. Best iteration: [1] train-rmse:2783784.250000 test-rmse:1840767.875000 [1] train-rmse:2365927.000000 test-rmse:3617987.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2004014.625000 test-rmse:3602716.500000 [3] train-rmse:1708080.750000 test-rmse:3595457.250000 [4] train-rmse:1466650.250000 test-rmse:3594038.750000 [5] train-rmse:1270168.125000 test-rmse:3595568.250000 [6] train-rmse:1110938.125000 test-rmse:3593792.500000 [7] train-rmse:982983.687500 test-rmse:3594380.750000 [8] train-rmse:880018.812500 test-rmse:3595194.500000 [9] train-rmse:798503.500000 test-rmse:3598152.500000 Stopping. Best iteration: [6] train-rmse:1110938.125000 test-rmse:3593792.500000 [1] train-rmse:2810201.750000 test-rmse:1209556.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2385061.750000 test-rmse:2103045.500000 [3] train-rmse:2050985.625000 test-rmse:2872837.750000 [4] train-rmse:1789246.875000 test-rmse:3513380.500000 Stopping. Best iteration: [1] train-rmse:2810201.750000 test-rmse:1209556.750000 [1] train-rmse:2871819.250000 test-rmse:1215511.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2545675.000000 test-rmse:1171965.625000 [3] train-rmse:2298627.250000 test-rmse:1180761.875000 [4] train-rmse:2113802.000000 test-rmse:1191703.500000 [5] train-rmse:1977523.125000 test-rmse:1184893.250000 Stopping. Best iteration: [2] train-rmse:2545675.000000 test-rmse:1171965.625000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:907531.062500 test-rmse:991896.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:803805.500000 test-rmse:946356.812500 [3] train-rmse:745185.937500 test-rmse:929166.750000 [4] train-rmse:713405.625000 test-rmse:922373.000000 [5] train-rmse:696441.250000 test-rmse:919762.750000 [6] train-rmse:687213.875000 test-rmse:918918.687500 [7] train-rmse:682223.812500 test-rmse:919693.687500 [8] train-rmse:679642.187500 test-rmse:921035.875000 [9] train-rmse:678220.312500 test-rmse:920862.750000 Stopping. Best iteration: [6] train-rmse:687213.875000 test-rmse:918918.687500 [1] train-rmse:765598.750000 test-rmse:1423663.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:646238.187500 test-rmse:1385953.125000 [3] train-rmse:572603.312500 test-rmse:1362525.000000 [4] train-rmse:527354.687500 test-rmse:1337337.250000 [5] train-rmse:500102.062500 test-rmse:1321131.375000 [6] train-rmse:482949.375000 test-rmse:1310564.625000 [7] train-rmse:472917.031250 test-rmse:1310504.375000 [8] train-rmse:465947.343750 test-rmse:1300858.875000 [9] train-rmse:461213.562500 test-rmse:1296542.625000 [10] train-rmse:458215.125000 test-rmse:1290610.125000 [11] train-rmse:456151.562500 test-rmse:1286243.500000 [12] train-rmse:454534.281250 test-rmse:1284374.875000 [13] train-rmse:453443.656250 test-rmse:1284517.125000 [14] train-rmse:452649.718750 test-rmse:1283005.375000 [15] train-rmse:452113.062500 test-rmse:1282536.625000 [16] train-rmse:451736.562500 test-rmse:1282542.375000 [17] train-rmse:451458.781250 test-rmse:1282298.875000 [18] train-rmse:451092.343750 test-rmse:1281764.000000 [19] train-rmse:450815.156250 test-rmse:1281708.125000 [20] train-rmse:450702.000000 test-rmse:1281492.750000 [1] train-rmse:903497.437500 test-rmse:1013628.312500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:814417.812500 test-rmse:863058.562500 [3] train-rmse:760515.750000 test-rmse:806794.937500 [4] train-rmse:728342.562500 test-rmse:767515.375000 [5] train-rmse:708603.562500 test-rmse:705430.812500 [6] train-rmse:696234.875000 test-rmse:666989.625000 [7] train-rmse:688428.937500 test-rmse:643396.062500 [8] train-rmse:683356.625000 test-rmse:629261.000000 [9] train-rmse:680095.000000 test-rmse:623532.437500 [10] train-rmse:677909.062500 test-rmse:617286.687500 [11] train-rmse:676374.250000 test-rmse:614145.062500 [12] train-rmse:675261.062500 test-rmse:612479.625000 [13] train-rmse:674484.375000 test-rmse:611617.687500 [14] train-rmse:673912.250000 test-rmse:611527.687500 [15] train-rmse:673473.000000 test-rmse:611554.687500 [16] train-rmse:673192.875000 test-rmse:611498.687500 [17] train-rmse:672891.437500 test-rmse:611238.062500 [18] train-rmse:672732.812500 test-rmse:611234.250000 [19] train-rmse:672606.437500 test-rmse:611166.062500 [20] train-rmse:672519.875000 test-rmse:611143.187500 [1] train-rmse:992155.875000 test-rmse:529638.562500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:892243.500000 test-rmse:460899.281250 [3] train-rmse:825900.000000 test-rmse:414087.968750 [4] train-rmse:784792.687500 test-rmse:390763.000000 [5] train-rmse:760657.687500 test-rmse:380925.312500 [6] train-rmse:746101.937500 test-rmse:374188.343750 [7] train-rmse:736197.000000 test-rmse:370405.593750 [8] train-rmse:729929.125000 test-rmse:366921.468750 [9] train-rmse:725807.000000 test-rmse:365374.468750 [10] train-rmse:723096.062500 test-rmse:362727.812500 [11] train-rmse:721244.312500 test-rmse:361538.093750 [12] train-rmse:719987.375000 test-rmse:361702.000000 [13] train-rmse:719123.062500 test-rmse:360711.000000 [14] train-rmse:718513.625000 test-rmse:360261.437500 [15] train-rmse:718100.562500 test-rmse:359329.875000 [16] train-rmse:717786.437500 test-rmse:359084.406250 [17] train-rmse:717538.375000 test-rmse:358798.906250 [18] train-rmse:717374.062500 test-rmse:359151.218750 [19] train-rmse:717230.937500 test-rmse:359130.406250 [20] train-rmse:717125.500000 test-rmse:359509.437500 Stopping. Best iteration: [17] train-rmse:717538.375000 test-rmse:358798.906250 [1] train-rmse:986103.500000 test-rmse:612149.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:882879.250000 test-rmse:673331.000000 [3] train-rmse:820085.312500 test-rmse:778166.000000 [4] train-rmse:782193.687500 test-rmse:892328.312500 Stopping. Best iteration: [1] train-rmse:986103.500000 test-rmse:612149.625000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:14780164.000000 test-rmse:3646065.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:14190717.000000 test-rmse:3502647.250000 [3] train-rmse:13791092.000000 test-rmse:3618938.750000 [4] train-rmse:13522116.000000 test-rmse:3908333.750000 [5] train-rmse:13379988.000000 test-rmse:4258624.500000 Stopping. Best iteration: [2] train-rmse:14190717.000000 test-rmse:3502647.250000 [1] train-rmse:14342233.000000 test-rmse:8177737.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:13796387.000000 test-rmse:8316248.500000 [3] train-rmse:13462925.000000 test-rmse:8572361.000000 [4] train-rmse:13264718.000000 test-rmse:8968611.000000 Stopping. Best iteration: [1] train-rmse:14342233.000000 test-rmse:8177737.500000 [1] train-rmse:7066117.500000 test-rmse:27080934.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6461032.500000 test-rmse:26828610.000000 [3] train-rmse:6164775.000000 test-rmse:26782416.000000 [4] train-rmse:5955701.500000 test-rmse:26761504.000000 [5] train-rmse:5699923.000000 test-rmse:26814188.000000 [6] train-rmse:5487831.000000 test-rmse:26839702.000000 [7] train-rmse:5339269.500000 test-rmse:26882078.000000 Stopping. Best iteration: [4] train-rmse:5955701.500000 test-rmse:26761504.000000 [1] train-rmse:13918331.000000 test-rmse:11515749.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:13410143.000000 test-rmse:11489968.000000 [3] train-rmse:13100283.000000 test-rmse:11498818.000000 [4] train-rmse:12921482.000000 test-rmse:11459200.000000 [5] train-rmse:12784663.000000 test-rmse:11537197.000000 [6] train-rmse:12724737.000000 test-rmse:11552255.000000 [7] train-rmse:12673160.000000 test-rmse:11583901.000000 Stopping. Best iteration: [4] train-rmse:12921482.000000 test-rmse:11459200.000000 [1] train-rmse:14779355.000000 test-rmse:4779232.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:14253672.000000 test-rmse:4798809.000000 [3] train-rmse:13947927.000000 test-rmse:4881149.000000 [4] train-rmse:13722252.000000 test-rmse:4941336.000000 Stopping. Best iteration: [1] train-rmse:14779355.000000 test-rmse:4779232.500000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:487945.031250 test-rmse:584987.812500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:370993.937500 test-rmse:455615.656250 [3] train-rmse:296175.125000 test-rmse:377518.468750 [4] train-rmse:250514.281250 test-rmse:334608.562500 [5] train-rmse:224295.921875 test-rmse:310896.531250 [6] train-rmse:209166.468750 test-rmse:295379.000000 [7] train-rmse:201351.093750 test-rmse:287370.875000 [8] train-rmse:196991.125000 test-rmse:283949.937500 [9] train-rmse:193982.718750 test-rmse:281877.218750 [10] train-rmse:192403.218750 test-rmse:282069.156250 [11] train-rmse:191562.656250 test-rmse:281549.156250 [12] train-rmse:191039.843750 test-rmse:281033.218750 [13] train-rmse:190521.031250 test-rmse:281383.625000 [14] train-rmse:190042.656250 test-rmse:280964.250000 [15] train-rmse:189871.062500 test-rmse:281078.218750 [16] train-rmse:189645.703125 test-rmse:281130.593750 [17] train-rmse:189525.328125 test-rmse:281214.406250 Stopping. Best iteration: [14] train-rmse:190042.656250 test-rmse:280964.250000 [1] train-rmse:469734.187500 test-rmse:672291.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:361022.937500 test-rmse:536845.500000 [3] train-rmse:292312.500000 test-rmse:451949.062500 [4] train-rmse:251132.312500 test-rmse:401448.968750 [5] train-rmse:227720.218750 test-rmse:372295.875000 [6] train-rmse:213442.453125 test-rmse:355462.906250 [7] train-rmse:205963.484375 test-rmse:344473.937500 [8] train-rmse:201932.765625 test-rmse:338718.656250 [9] train-rmse:199545.375000 test-rmse:335755.937500 [10] train-rmse:198416.781250 test-rmse:331261.312500 [11] train-rmse:197268.093750 test-rmse:328670.406250 [12] train-rmse:196717.984375 test-rmse:326763.875000 [13] train-rmse:196462.796875 test-rmse:326539.250000 [14] train-rmse:196252.609375 test-rmse:326134.656250 [15] train-rmse:196013.578125 test-rmse:325923.000000 [16] train-rmse:195737.406250 test-rmse:325789.468750 [17] train-rmse:195654.078125 test-rmse:326496.406250 [18] train-rmse:195522.296875 test-rmse:327393.250000 [19] train-rmse:195460.437500 test-rmse:327952.125000 Stopping. Best iteration: [16] train-rmse:195737.406250 test-rmse:325789.468750 [1] train-rmse:502209.968750 test-rmse:556298.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:381615.562500 test-rmse:455642.125000 [3] train-rmse:304522.781250 test-rmse:390173.250000 [4] train-rmse:257553.703125 test-rmse:352611.687500 [5] train-rmse:230035.281250 test-rmse:329710.750000 [6] train-rmse:214768.281250 test-rmse:317598.750000 [7] train-rmse:206646.312500 test-rmse:311802.031250 [8] train-rmse:202357.640625 test-rmse:308266.062500 [9] train-rmse:200049.234375 test-rmse:306466.375000 [10] train-rmse:198748.984375 test-rmse:302808.875000 [11] train-rmse:197957.890625 test-rmse:302438.593750 [12] train-rmse:197412.046875 test-rmse:302365.281250 [13] train-rmse:196757.500000 test-rmse:300583.843750 [14] train-rmse:196487.125000 test-rmse:299098.000000 [15] train-rmse:196241.000000 test-rmse:299538.500000 [16] train-rmse:196068.140625 test-rmse:300045.406250 [17] train-rmse:195888.734375 test-rmse:299641.531250 Stopping. Best iteration: [14] train-rmse:196487.125000 test-rmse:299098.000000 [1] train-rmse:541867.750000 test-rmse:339414.906250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:413853.250000 test-rmse:255533.218750 [3] train-rmse:331778.718750 test-rmse:195086.046875 [4] train-rmse:282108.343750 test-rmse:166765.218750 [5] train-rmse:253361.390625 test-rmse:150993.562500 [6] train-rmse:237286.437500 test-rmse:144751.125000 [7] train-rmse:228326.421875 test-rmse:142856.515625 [8] train-rmse:223742.343750 test-rmse:142949.359375 [9] train-rmse:221167.484375 test-rmse:145645.140625 [10] train-rmse:219645.078125 test-rmse:146704.250000 Stopping. Best iteration: [7] train-rmse:228326.421875 test-rmse:142856.515625 [1] train-rmse:534354.000000 test-rmse:379200.093750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:408471.718750 test-rmse:282138.187500 [3] train-rmse:328658.156250 test-rmse:226135.546875 [4] train-rmse:279082.093750 test-rmse:195184.140625 [5] train-rmse:250695.093750 test-rmse:178035.703125 [6] train-rmse:234843.109375 test-rmse:172412.312500 [7] train-rmse:225427.500000 test-rmse:168468.515625 [8] train-rmse:220584.453125 test-rmse:167502.656250 [9] train-rmse:217756.562500 test-rmse:166352.328125 [10] train-rmse:216016.531250 test-rmse:166194.531250 [11] train-rmse:215021.062500 test-rmse:166731.156250 [12] train-rmse:214534.125000 test-rmse:166783.234375 [13] train-rmse:213996.796875 test-rmse:167237.421875 Stopping. Best iteration: [10] train-rmse:216016.531250 test-rmse:166194.531250
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:570781.500000 test-rmse:642526.562500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:444411.437500 test-rmse:487968.843750 [3] train-rmse:365822.000000 test-rmse:402141.156250 [4] train-rmse:319413.625000 test-rmse:364873.531250 [5] train-rmse:293524.906250 test-rmse:350457.906250 [6] train-rmse:279563.781250 test-rmse:347787.968750 [7] train-rmse:272106.781250 test-rmse:350169.750000 [8] train-rmse:268224.375000 test-rmse:353670.375000 [9] train-rmse:266093.000000 test-rmse:357499.562500 Stopping. Best iteration: [6] train-rmse:279563.781250 test-rmse:347787.968750 [1] train-rmse:572330.250000 test-rmse:690395.312500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:448884.062500 test-rmse:547657.062500 [3] train-rmse:372529.031250 test-rmse:463490.375000 [4] train-rmse:327500.000000 test-rmse:407610.593750 [5] train-rmse:302646.531250 test-rmse:375029.718750 [6] train-rmse:289116.812500 test-rmse:356137.812500 [7] train-rmse:281967.718750 test-rmse:344553.312500 [8] train-rmse:278147.343750 test-rmse:337929.781250 [9] train-rmse:276181.343750 test-rmse:333365.281250 [10] train-rmse:275045.687500 test-rmse:331973.562500 [11] train-rmse:274400.500000 test-rmse:330379.406250 [12] train-rmse:273956.000000 test-rmse:329474.468750 [13] train-rmse:273682.500000 test-rmse:328573.406250 [14] train-rmse:273533.500000 test-rmse:328290.718750 [15] train-rmse:273399.406250 test-rmse:328378.093750 [16] train-rmse:273315.906250 test-rmse:328945.281250 [17] train-rmse:273244.000000 test-rmse:328915.187500 Stopping. Best iteration: [14] train-rmse:273533.500000 test-rmse:328290.718750 [1] train-rmse:581963.375000 test-rmse:638871.312500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:450961.062500 test-rmse:522591.968750 [3] train-rmse:368962.000000 test-rmse:453583.500000 [4] train-rmse:320756.812500 test-rmse:412168.187500 [5] train-rmse:293551.781250 test-rmse:392147.031250 [6] train-rmse:278882.281250 test-rmse:379317.250000 [7] train-rmse:271126.312500 test-rmse:373571.531250 [8] train-rmse:266902.750000 test-rmse:370402.218750 [9] train-rmse:264705.562500 test-rmse:368508.687500 [10] train-rmse:263540.093750 test-rmse:366684.062500 [11] train-rmse:262883.812500 test-rmse:366437.656250 [12] train-rmse:262493.343750 test-rmse:366010.468750 [13] train-rmse:262249.687500 test-rmse:365430.937500 [14] train-rmse:262072.187500 test-rmse:365525.281250 [15] train-rmse:261938.593750 test-rmse:365519.156250 [16] train-rmse:261833.421875 test-rmse:367057.781250 Stopping. Best iteration: [13] train-rmse:262249.687500 test-rmse:365430.937500 [1] train-rmse:619022.875000 test-rmse:474307.531250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:481952.906250 test-rmse:372699.812500 [3] train-rmse:396847.031250 test-rmse:314310.125000 [4] train-rmse:346239.687500 test-rmse:278345.968750 [5] train-rmse:317844.281250 test-rmse:262681.593750 [6] train-rmse:302707.593750 test-rmse:252382.843750 [7] train-rmse:294326.468750 test-rmse:247371.765625 [8] train-rmse:289994.375000 test-rmse:244354.046875 [9] train-rmse:287713.875000 test-rmse:242814.328125 [10] train-rmse:286154.375000 test-rmse:242420.296875 [11] train-rmse:285413.125000 test-rmse:242804.828125 [12] train-rmse:284792.218750 test-rmse:242489.828125 [13] train-rmse:284393.187500 test-rmse:242366.171875 [14] train-rmse:284203.343750 test-rmse:242246.328125 [15] train-rmse:283989.531250 test-rmse:242077.125000 [16] train-rmse:283881.375000 test-rmse:242250.171875 [17] train-rmse:283769.125000 test-rmse:242353.953125 [18] train-rmse:283696.406250 test-rmse:242486.265625 Stopping. Best iteration: [15] train-rmse:283989.531250 test-rmse:242077.125000 [1] train-rmse:613696.000000 test-rmse:502494.281250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:479884.843750 test-rmse:394478.875000 [3] train-rmse:396798.375000 test-rmse:328843.531250 [4] train-rmse:347845.718750 test-rmse:289860.843750 [5] train-rmse:320733.531250 test-rmse:264272.062500 [6] train-rmse:305861.562500 test-rmse:249241.203125 [7] train-rmse:298180.343750 test-rmse:242656.296875 [8] train-rmse:294048.000000 test-rmse:239229.718750 [9] train-rmse:291926.812500 test-rmse:236618.625000 [10] train-rmse:290823.906250 test-rmse:235212.078125 [11] train-rmse:290083.281250 test-rmse:235046.859375 [12] train-rmse:289609.875000 test-rmse:234441.875000 [13] train-rmse:289323.531250 test-rmse:234739.125000 [14] train-rmse:289128.312500 test-rmse:235134.531250 [15] train-rmse:289064.343750 test-rmse:235254.656250 Stopping. Best iteration: [12] train-rmse:289609.875000 test-rmse:234441.875000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:544016.625000 test-rmse:355413.593750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:460638.968750 test-rmse:271826.781250 [3] train-rmse:398143.656250 test-rmse:227381.937500 [4] train-rmse:353674.750000 test-rmse:190304.390625 [5] train-rmse:320672.062500 test-rmse:181450.718750 [6] train-rmse:298093.000000 test-rmse:173805.781250 [7] train-rmse:281211.875000 test-rmse:177632.343750 [8] train-rmse:269592.781250 test-rmse:178269.921875 [9] train-rmse:260832.593750 test-rmse:179039.328125 Stopping. Best iteration: [6] train-rmse:298093.000000 test-rmse:173805.781250 [1] train-rmse:569782.937500 test-rmse:134327.921875 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:480534.187500 test-rmse:97179.671875 [3] train-rmse:414493.125000 test-rmse:79181.039062 [4] train-rmse:366829.500000 test-rmse:73943.046875 [5] train-rmse:333248.906250 test-rmse:76351.335938 [6] train-rmse:309014.281250 test-rmse:81784.218750 [7] train-rmse:291736.718750 test-rmse:88543.460938 Stopping. Best iteration: [4] train-rmse:366829.500000 test-rmse:73943.046875 [1] train-rmse:537954.750000 test-rmse:317495.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:453884.125000 test-rmse:213818.421875 [3] train-rmse:382625.218750 test-rmse:177508.015625 [4] train-rmse:328747.281250 test-rmse:216885.750000 [5] train-rmse:285369.625000 test-rmse:271661.437500 [6] train-rmse:251670.156250 test-rmse:325493.000000 Stopping. Best iteration: [3] train-rmse:382625.218750 test-rmse:177508.015625 [1] train-rmse:373157.687500 test-rmse:947697.437500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:297113.218750 test-rmse:887108.500000 [3] train-rmse:242222.265625 test-rmse:854451.562500 [4] train-rmse:205397.765625 test-rmse:829546.000000 [5] train-rmse:181012.093750 test-rmse:807824.750000 [6] train-rmse:162582.750000 test-rmse:802433.687500 [7] train-rmse:151396.250000 test-rmse:789497.250000 [8] train-rmse:144179.937500 test-rmse:779664.187500 [9] train-rmse:137564.609375 test-rmse:778957.625000 [10] train-rmse:133924.078125 test-rmse:774206.062500 [11] train-rmse:131113.843750 test-rmse:769821.437500 [12] train-rmse:129355.273438 test-rmse:766431.750000 [13] train-rmse:128243.015625 test-rmse:763356.687500 [14] train-rmse:127443.781250 test-rmse:759880.437500 [15] train-rmse:126914.515625 test-rmse:756365.437500 [16] train-rmse:126588.750000 test-rmse:754077.812500 [17] train-rmse:126344.328125 test-rmse:752063.062500 [18] train-rmse:126163.804688 test-rmse:750932.625000 [19] train-rmse:126001.445312 test-rmse:749781.750000 [20] train-rmse:125596.062500 test-rmse:749923.625000 [1] train-rmse:514134.593750 test-rmse:467430.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:436431.250000 test-rmse:362349.000000 [3] train-rmse:374674.218750 test-rmse:323292.781250 [4] train-rmse:330605.281250 test-rmse:314382.406250 [5] train-rmse:298755.437500 test-rmse:320014.781250 [6] train-rmse:275987.750000 test-rmse:330036.562500 [7] train-rmse:259727.359375 test-rmse:343442.500000 Stopping. Best iteration: [4] train-rmse:330605.281250 test-rmse:314382.406250
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:197759.437500 test-rmse:117078.296875 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:154051.937500 test-rmse:66769.843750 [3] train-rmse:124926.757812 test-rmse:44574.132812 [4] train-rmse:106305.242188 test-rmse:49866.984375 [5] train-rmse:94933.296875 test-rmse:64016.640625 [6] train-rmse:88275.265625 test-rmse:77155.679688 Stopping. Best iteration: [3] train-rmse:124926.757812 test-rmse:44574.132812 [1] train-rmse:199167.562500 test-rmse:136166.265625 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:156948.156250 test-rmse:97156.679688 [3] train-rmse:128694.875000 test-rmse:72795.656250 [4] train-rmse:110513.804688 test-rmse:59629.863281 [5] train-rmse:99272.351562 test-rmse:53726.796875 [6] train-rmse:92609.718750 test-rmse:52128.730469 [7] train-rmse:88776.390625 test-rmse:52463.792969 [8] train-rmse:86610.062500 test-rmse:53427.472656 [9] train-rmse:85406.226562 test-rmse:54480.539062 Stopping. Best iteration: [6] train-rmse:92609.718750 test-rmse:52128.730469 [1] train-rmse:195234.937500 test-rmse:165874.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:154006.078125 test-rmse:128471.812500 [3] train-rmse:126368.976562 test-rmse:103912.437500 [4] train-rmse:108525.406250 test-rmse:88585.578125 [5] train-rmse:97487.679688 test-rmse:79076.867188 [6] train-rmse:90928.187500 test-rmse:73469.234375 [7] train-rmse:87138.132812 test-rmse:70249.968750 [8] train-rmse:84995.210938 test-rmse:68351.226562 [9] train-rmse:83800.351562 test-rmse:67252.789062 [10] train-rmse:83128.875000 test-rmse:66584.507812 [11] train-rmse:82757.429688 test-rmse:66182.187500 [12] train-rmse:82551.734375 test-rmse:65924.546875 [13] train-rmse:82437.929688 test-rmse:65759.296875 [14] train-rmse:82374.890625 test-rmse:65652.515625 [15] train-rmse:82339.867188 test-rmse:65579.578125 [16] train-rmse:82320.406250 test-rmse:65530.097656 [17] train-rmse:82309.570312 test-rmse:65496.625000 [18] train-rmse:82303.562500 test-rmse:65472.910156 [19] train-rmse:82300.148438 test-rmse:65456.351562 [20] train-rmse:82298.234375 test-rmse:65444.910156 [1] train-rmse:172182.234375 test-rmse:266023.437500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:139364.890625 test-rmse:224431.343750 [3] train-rmse:115669.453125 test-rmse:189134.312500 [4] train-rmse:99443.593750 test-rmse:159928.625000 [5] train-rmse:88614.000000 test-rmse:140773.875000 [6] train-rmse:81558.914062 test-rmse:129061.960938 [7] train-rmse:76977.710938 test-rmse:123408.335938 [8] train-rmse:74038.492188 test-rmse:121077.218750 [9] train-rmse:72167.882812 test-rmse:120663.804688 [10] train-rmse:70984.812500 test-rmse:121316.289062 [11] train-rmse:70238.367188 test-rmse:122481.867188 [12] train-rmse:69767.468750 test-rmse:123794.671875 Stopping. Best iteration: [9] train-rmse:72167.882812 test-rmse:120663.804688 [1] train-rmse:181727.984375 test-rmse:230105.890625 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:141047.734375 test-rmse:192462.578125 [3] train-rmse:114277.593750 test-rmse:175028.156250 [4] train-rmse:97380.125000 test-rmse:169802.015625 [5] train-rmse:86982.585938 test-rmse:171421.171875 [6] train-rmse:80717.179688 test-rmse:176777.750000 [7] train-rmse:77044.617188 test-rmse:183170.171875 Stopping. Best iteration: [4] train-rmse:97380.125000 test-rmse:169802.015625
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:461487.968750 test-rmse:234660.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:429372.593750 test-rmse:179350.609375 [3] train-rmse:412150.718750 test-rmse:147300.531250 [4] train-rmse:402769.968750 test-rmse:127062.515625 [5] train-rmse:397870.156250 test-rmse:119944.023438 [6] train-rmse:395353.625000 test-rmse:117485.171875 [7] train-rmse:394034.218750 test-rmse:117000.414062 [8] train-rmse:393310.687500 test-rmse:116488.062500 [9] train-rmse:392923.750000 test-rmse:117574.234375 [10] train-rmse:392719.718750 test-rmse:117930.687500 [11] train-rmse:392601.281250 test-rmse:119669.156250 Stopping. Best iteration: [8] train-rmse:393310.687500 test-rmse:116488.062500 [1] train-rmse:461213.125000 test-rmse:230588.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:428920.375000 test-rmse:184798.828125 [3] train-rmse:411548.718750 test-rmse:157238.468750 [4] train-rmse:402526.312500 test-rmse:144493.015625 [5] train-rmse:397853.531250 test-rmse:134293.140625 [6] train-rmse:395468.625000 test-rmse:129908.539062 [7] train-rmse:394173.937500 test-rmse:128310.343750 [8] train-rmse:393512.375000 test-rmse:126337.281250 [9] train-rmse:393135.062500 test-rmse:125104.914062 [10] train-rmse:392878.187500 test-rmse:124172.000000 [11] train-rmse:392739.250000 test-rmse:123694.203125 [12] train-rmse:392636.093750 test-rmse:123197.937500 [13] train-rmse:392577.656250 test-rmse:123042.890625 [14] train-rmse:392554.687500 test-rmse:122943.257812 [15] train-rmse:392535.843750 test-rmse:123216.164062 [16] train-rmse:392524.093750 test-rmse:123362.773438 [17] train-rmse:392516.406250 test-rmse:123469.132812 Stopping. Best iteration: [14] train-rmse:392554.687500 test-rmse:122943.257812 [1] train-rmse:456491.031250 test-rmse:276545.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:425940.906250 test-rmse:224413.312500 [3] train-rmse:409784.093750 test-rmse:193023.296875 [4] train-rmse:401293.562500 test-rmse:172208.296875 [5] train-rmse:396929.718750 test-rmse:161029.031250 [6] train-rmse:394639.875000 test-rmse:152374.875000 [7] train-rmse:393456.062500 test-rmse:147401.328125 [8] train-rmse:392840.843750 test-rmse:144582.250000 [9] train-rmse:392526.093750 test-rmse:142995.406250 [10] train-rmse:392333.281250 test-rmse:142198.828125 [11] train-rmse:392232.843750 test-rmse:141746.906250 [12] train-rmse:392178.187500 test-rmse:141627.328125 [13] train-rmse:392072.906250 test-rmse:141579.156250 [14] train-rmse:391991.562500 test-rmse:141414.828125 [15] train-rmse:391945.062500 test-rmse:141512.890625 [16] train-rmse:391913.937500 test-rmse:141659.640625 [17] train-rmse:391892.718750 test-rmse:141642.640625 Stopping. Best iteration: [14] train-rmse:391991.562500 test-rmse:141414.828125 [1] train-rmse:242397.484375 test-rmse:838488.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:182140.468750 test-rmse:828925.375000 [3] train-rmse:141549.656250 test-rmse:823712.312500 [4] train-rmse:115760.414062 test-rmse:821189.812500 [5] train-rmse:99121.710938 test-rmse:819959.062500 [6] train-rmse:88700.851562 test-rmse:818409.875000 [7] train-rmse:82993.117188 test-rmse:817079.000000 [8] train-rmse:79345.312500 test-rmse:816249.750000 [9] train-rmse:77179.648438 test-rmse:815215.812500 [10] train-rmse:76096.062500 test-rmse:814815.437500 [11] train-rmse:75490.570312 test-rmse:814542.500000 [12] train-rmse:75145.585938 test-rmse:814361.687500 [13] train-rmse:74889.898438 test-rmse:814246.000000 [14] train-rmse:74501.718750 test-rmse:814156.562500 [15] train-rmse:74283.671875 test-rmse:814101.937500 [16] train-rmse:74216.085938 test-rmse:813270.312500 [17] train-rmse:74084.406250 test-rmse:813244.937500 [18] train-rmse:74050.289062 test-rmse:812554.062500 [19] train-rmse:73973.765625 test-rmse:812768.062500 [20] train-rmse:73945.578125 test-rmse:812776.187500 [1] train-rmse:458400.218750 test-rmse:244957.765625 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:427390.000000 test-rmse:188761.968750 [3] train-rmse:410900.156250 test-rmse:157666.953125 [4] train-rmse:402280.468750 test-rmse:143797.500000 [5] train-rmse:397709.250000 test-rmse:141153.375000 [6] train-rmse:395350.062500 test-rmse:141397.828125 [7] train-rmse:394098.312500 test-rmse:144862.468750 [8] train-rmse:393454.593750 test-rmse:147371.015625 Stopping. Best iteration: [5] train-rmse:397709.250000 test-rmse:141153.375000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:1712099.875000 test-rmse:10610785.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1487928.250000 test-rmse:10581957.000000 [3] train-rmse:1330445.750000 test-rmse:10299444.000000 [4] train-rmse:1219383.000000 test-rmse:10079225.000000 [5] train-rmse:1143377.875000 test-rmse:10075780.000000 [6] train-rmse:1088605.250000 test-rmse:10074536.000000 [7] train-rmse:1029789.187500 test-rmse:10074235.000000 [8] train-rmse:989705.375000 test-rmse:10074907.000000 [9] train-rmse:961205.687500 test-rmse:10075336.000000 [10] train-rmse:941328.875000 test-rmse:10076025.000000 Stopping. Best iteration: [7] train-rmse:1029789.187500 test-rmse:10074235.000000 [1] train-rmse:4946422.500000 test-rmse:1230471.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4339546.500000 test-rmse:1180835.125000 [3] train-rmse:3914754.250000 test-rmse:1176479.000000 [4] train-rmse:3631985.750000 test-rmse:1202514.000000 [5] train-rmse:3449498.250000 test-rmse:1202584.250000 [6] train-rmse:3332735.500000 test-rmse:1228482.750000 Stopping. Best iteration: [3] train-rmse:3914754.250000 test-rmse:1176479.000000 [1] train-rmse:4924430.000000 test-rmse:1399103.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4310763.000000 test-rmse:1282459.375000 [3] train-rmse:3894560.000000 test-rmse:1259660.500000 [4] train-rmse:3619323.500000 test-rmse:1280699.375000 [5] train-rmse:3441292.250000 test-rmse:1321008.500000 [6] train-rmse:3327979.750000 test-rmse:1368710.750000 Stopping. Best iteration: [3] train-rmse:3894560.000000 test-rmse:1259660.500000 [1] train-rmse:4941031.000000 test-rmse:1241727.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4326208.500000 test-rmse:1183496.500000 [3] train-rmse:3909041.250000 test-rmse:1128285.000000 [4] train-rmse:3634168.750000 test-rmse:1107422.500000 [5] train-rmse:3456255.250000 test-rmse:1091617.500000 [6] train-rmse:3343004.750000 test-rmse:1083398.375000 [7] train-rmse:3272514.000000 test-rmse:1076486.750000 [8] train-rmse:3227668.250000 test-rmse:1082935.250000 [9] train-rmse:3199298.500000 test-rmse:1099363.000000 [10] train-rmse:3178872.000000 test-rmse:1120199.875000 Stopping. Best iteration: [7] train-rmse:3272514.000000 test-rmse:1076486.750000 [1] train-rmse:4797912.000000 test-rmse:2893374.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4208192.000000 test-rmse:2696102.000000 [3] train-rmse:3811600.750000 test-rmse:2569593.250000 [4] train-rmse:3550785.250000 test-rmse:2475738.750000 [5] train-rmse:3385469.500000 test-rmse:2416748.250000 [6] train-rmse:3279725.500000 test-rmse:2386116.250000 [7] train-rmse:3213477.500000 test-rmse:2375864.500000 [8] train-rmse:3173603.250000 test-rmse:2354202.500000 [9] train-rmse:3147266.000000 test-rmse:2345982.250000 [10] train-rmse:3131827.000000 test-rmse:2341095.750000 [11] train-rmse:3121369.000000 test-rmse:2344034.500000 [12] train-rmse:3112481.750000 test-rmse:2363634.500000 [13] train-rmse:3107437.250000 test-rmse:2381622.000000 Stopping. Best iteration: [10] train-rmse:3131827.000000 test-rmse:2341095.750000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:454777.468750 test-rmse:349818.531250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:367748.000000 test-rmse:260058.062500 [3] train-rmse:315013.968750 test-rmse:205199.609375 [4] train-rmse:284267.062500 test-rmse:174287.000000 [5] train-rmse:267368.906250 test-rmse:158794.109375 [6] train-rmse:258209.265625 test-rmse:152121.171875 [7] train-rmse:253306.484375 test-rmse:150209.218750 [8] train-rmse:250714.156250 test-rmse:150331.375000 [9] train-rmse:249332.093750 test-rmse:151252.281250 [10] train-rmse:248591.671875 test-rmse:151804.062500 Stopping. Best iteration: [7] train-rmse:253306.484375 test-rmse:150209.218750 [1] train-rmse:445083.937500 test-rmse:410443.781250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:359585.156250 test-rmse:321942.875000 [3] train-rmse:307541.781250 test-rmse:264972.906250 [4] train-rmse:277392.093750 test-rmse:231187.609375 [5] train-rmse:260635.593750 test-rmse:212068.046875 [6] train-rmse:251581.625000 test-rmse:201837.093750 [7] train-rmse:246771.140625 test-rmse:196633.125000 [8] train-rmse:244213.609375 test-rmse:194073.015625 [9] train-rmse:242759.500000 test-rmse:192439.093750 [10] train-rmse:241987.312500 test-rmse:191888.421875 [11] train-rmse:241559.250000 test-rmse:192595.781250 [12] train-rmse:241323.890625 test-rmse:193582.984375 [13] train-rmse:241167.671875 test-rmse:193469.437500 Stopping. Best iteration: [10] train-rmse:241987.312500 test-rmse:191888.421875 [1] train-rmse:450333.156250 test-rmse:377666.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:362450.500000 test-rmse:293180.312500 [3] train-rmse:308679.531250 test-rmse:243669.515625 [4] train-rmse:277440.781250 test-rmse:218479.656250 [5] train-rmse:260022.890625 test-rmse:207246.015625 [6] train-rmse:250668.562500 test-rmse:201964.015625 [7] train-rmse:245539.062500 test-rmse:200103.171875 [8] train-rmse:242842.453125 test-rmse:199756.640625 [9] train-rmse:241422.843750 test-rmse:199715.921875 [10] train-rmse:240684.125000 test-rmse:200228.062500 [11] train-rmse:240280.765625 test-rmse:200771.265625 [12] train-rmse:240065.234375 test-rmse:201158.343750 Stopping. Best iteration: [9] train-rmse:241422.843750 test-rmse:199715.921875 [1] train-rmse:443391.500000 test-rmse:424512.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:359255.687500 test-rmse:334110.406250 [3] train-rmse:308245.406250 test-rmse:276260.187500 [4] train-rmse:278670.843750 test-rmse:242470.640625 [5] train-rmse:262287.000000 test-rmse:223194.609375 [6] train-rmse:253340.125000 test-rmse:210217.281250 [7] train-rmse:248558.687500 test-rmse:202802.921875 [8] train-rmse:246040.734375 test-rmse:199736.921875 [9] train-rmse:244660.703125 test-rmse:198450.562500 [10] train-rmse:243940.343750 test-rmse:198024.968750 [11] train-rmse:243546.437500 test-rmse:197156.218750 [12] train-rmse:243315.187500 test-rmse:196900.640625 [13] train-rmse:243184.015625 test-rmse:196776.906250 [14] train-rmse:243105.578125 test-rmse:196024.890625 [15] train-rmse:243062.531250 test-rmse:196038.203125 [16] train-rmse:243032.015625 test-rmse:195626.828125 [17] train-rmse:243018.843750 test-rmse:195328.156250 [18] train-rmse:243005.171875 test-rmse:195178.015625 [19] train-rmse:243000.000000 test-rmse:195060.468750 [20] train-rmse:242993.703125 test-rmse:195045.968750 [1] train-rmse:389216.437500 test-rmse:612428.437500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:299093.750000 test-rmse:547199.750000 [3] train-rmse:242712.015625 test-rmse:511114.062500 [4] train-rmse:208956.640625 test-rmse:490508.687500 [5] train-rmse:190083.421875 test-rmse:479201.375000 [6] train-rmse:179887.218750 test-rmse:473359.031250 [7] train-rmse:174535.765625 test-rmse:470294.031250 [8] train-rmse:171687.140625 test-rmse:467759.750000 [9] train-rmse:170201.734375 test-rmse:466175.125000 [10] train-rmse:169446.203125 test-rmse:465667.656250 [11] train-rmse:169067.203125 test-rmse:465427.156250 [12] train-rmse:168864.906250 test-rmse:465187.500000 [13] train-rmse:168719.578125 test-rmse:464949.750000 [14] train-rmse:168644.015625 test-rmse:464802.031250 [15] train-rmse:168616.625000 test-rmse:464740.656250 [16] train-rmse:168581.078125 test-rmse:464663.093750 [17] train-rmse:168570.546875 test-rmse:464655.468750 [18] train-rmse:168529.718750 test-rmse:464196.750000 [19] train-rmse:168521.984375 test-rmse:464211.218750 [20] train-rmse:168504.437500 test-rmse:463999.843750
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:452676.906250 test-rmse:468433.281250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:346289.625000 test-rmse:360062.562500 [3] train-rmse:279332.125000 test-rmse:294823.500000 [4] train-rmse:238669.750000 test-rmse:252006.265625 [5] train-rmse:215669.265625 test-rmse:230443.250000 [6] train-rmse:203157.640625 test-rmse:220645.687500 [7] train-rmse:196282.859375 test-rmse:216486.718750 [8] train-rmse:192732.812500 test-rmse:216089.593750 [9] train-rmse:190934.843750 test-rmse:217067.171875 [10] train-rmse:190023.921875 test-rmse:218331.390625 [11] train-rmse:189414.890625 test-rmse:218385.859375 Stopping. Best iteration: [8] train-rmse:192732.812500 test-rmse:216089.593750 [1] train-rmse:457304.281250 test-rmse:440562.468750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:349411.468750 test-rmse:333170.250000 [3] train-rmse:281111.218750 test-rmse:269866.625000 [4] train-rmse:240030.015625 test-rmse:236312.546875 [5] train-rmse:216546.125000 test-rmse:221060.875000 [6] train-rmse:203906.593750 test-rmse:216091.750000 [7] train-rmse:197290.671875 test-rmse:215139.468750 [8] train-rmse:193768.296875 test-rmse:215634.968750 [9] train-rmse:191692.250000 test-rmse:217551.734375 [10] train-rmse:190652.343750 test-rmse:219160.140625 Stopping. Best iteration: [7] train-rmse:197290.671875 test-rmse:215139.468750 [1] train-rmse:460777.937500 test-rmse:436471.031250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:351996.218750 test-rmse:338546.687500 [3] train-rmse:282894.750000 test-rmse:275596.562500 [4] train-rmse:241096.984375 test-rmse:242567.406250 [5] train-rmse:217110.609375 test-rmse:224713.515625 [6] train-rmse:204134.093750 test-rmse:216143.812500 [7] train-rmse:197167.531250 test-rmse:212210.781250 [8] train-rmse:193551.750000 test-rmse:210427.750000 [9] train-rmse:191729.203125 test-rmse:209593.125000 [10] train-rmse:190769.703125 test-rmse:209938.968750 [11] train-rmse:190270.906250 test-rmse:210130.500000 [12] train-rmse:190016.234375 test-rmse:210439.703125 Stopping. Best iteration: [9] train-rmse:191729.203125 test-rmse:209593.125000 [1] train-rmse:459108.750000 test-rmse:454610.187500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:353359.937500 test-rmse:355143.218750 [3] train-rmse:285233.125000 test-rmse:290225.968750 [4] train-rmse:243431.515625 test-rmse:243962.953125 [5] train-rmse:219686.375000 test-rmse:219481.843750 [6] train-rmse:206757.859375 test-rmse:207909.281250 [7] train-rmse:199970.093750 test-rmse:202724.062500 [8] train-rmse:196588.984375 test-rmse:200791.687500 [9] train-rmse:194723.546875 test-rmse:200605.593750 [10] train-rmse:193812.953125 test-rmse:200649.640625 [11] train-rmse:193318.484375 test-rmse:200927.484375 [12] train-rmse:193082.906250 test-rmse:201461.656250 Stopping. Best iteration: [9] train-rmse:194723.546875 test-rmse:200605.593750 [1] train-rmse:451617.406250 test-rmse:478098.687500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:346200.781250 test-rmse:369392.937500 [3] train-rmse:279702.656250 test-rmse:302272.812500 [4] train-rmse:239265.859375 test-rmse:247115.078125 [5] train-rmse:216141.375000 test-rmse:221966.953125 [6] train-rmse:203547.953125 test-rmse:216523.921875 [7] train-rmse:196853.406250 test-rmse:218751.406250 [8] train-rmse:193393.812500 test-rmse:221962.375000 [9] train-rmse:191548.421875 test-rmse:225097.640625 Stopping. Best iteration: [6] train-rmse:203547.953125 test-rmse:216523.921875
| borough | b_class_group | Feature | avg_gain |
|---|---|---|---|
| <int> | <chr> | <chr> | <dbl> |
| 1 | c | residentialunits_group | 0.2937428039 |
| 1 | c | zipcode | 0.4084550693 |
| 1 | c | commercialunits_group | 0.1715820831 |
| 1 | c | building_clusters | 0.0580763391 |
| 1 | c | address_encoded | 0.0168728132 |
| 1 | c | highly_commercial | 0.0469376615 |
| 1 | c | taxclass_present | 0.0043332299 |
| 1 | d | address_encoded | 0.2037620779 |
| 1 | d | zipcode | 0.3827269146 |
| 1 | d | commercialunits_group | 0.3224240439 |
| 1 | d | residentialunits_group | 0.0451528644 |
| 1 | d | highly_commercial | 0.0456401108 |
| 1 | d | building_clusters | 0.0003674855 |
| 1 | r | zipcode | 0.4861105953 |
| 1 | r | address_encoded | 0.1166941505 |
| 1 | r | highly_commercial | 0.0797937770 |
| 1 | r | building_clusters | 0.0707019925 |
| 1 | r | residentialunits_group | 0.1515167633 |
| 1 | r | onlycommercial | 0.0157512079 |
| 1 | r | taxclass_present | 0.0433420204 |
| 1 | r | commercialunits_group | 0.0360894932 |
| 1 | other | zipcode | 0.8477662618 |
| 1 | other | address_encoded | 0.0137963030 |
| 1 | other | highly_commercial | 0.0062322588 |
| 1 | other | commercialunits_group | 0.0297526775 |
| 1 | other | onlycommercial | 0.0037001928 |
| 1 | other | residentialunits_group | 0.0018897637 |
| 1 | other | taxclass_present | 0.0015418925 |
| 1 | other | building_clusters | 0.1588677497 |
| 1 | a | zipcode | 0.6324058083 |
| ... | ... | ... | ... |
| 5 | c | address_encoded | 0.1115904871 |
| 5 | c | zipcode | 0.1118430520 |
| 5 | c | highly_commercial | 0.0301853410 |
| 5 | c | residentialunits_group | 0.0144243963 |
| 5 | d | address_encoded | 0.9134361040 |
| 5 | d | zipcode | 0.0853566261 |
| 5 | d | residentialunits_group | 0.0228482439 |
| 5 | r | zipcode | 0.4993820216 |
| 5 | r | address_encoded | 0.2397504148 |
| 5 | r | taxclass_present | 0.1792777672 |
| 5 | r | residentialunits_group | 0.0300517269 |
| 5 | r | highly_commercial | 0.0644225869 |
| 5 | other | zipcode | 0.3435289516 |
| 5 | other | commercialunits_group | 0.0437824098 |
| 5 | other | address_encoded | 0.0437216460 |
| 5 | other | taxclass_present | 0.0396350011 |
| 5 | other | residentialunits_group | 0.0224033786 |
| 5 | other | building_clusters | 0.0114576346 |
| 5 | other | onlycommercial | 0.0006561068 |
| 5 | other | highly_commercial | 0.6244114334 |
| 5 | a | building_clusters | 0.5988855051 |
| 5 | a | zipcode | 0.2230675595 |
| 5 | a | address_encoded | 0.1772067039 |
| 5 | a | highly_commercial | 0.0004990452 |
| 5 | a | commercialunits_group | 0.0004264829 |
| 5 | b | zipcode | 0.9016850409 |
| 5 | b | address_encoded | 0.0762516788 |
| 5 | b | highly_commercial | 0.0052097205 |
| 5 | b | building_clusters | 0.0168088961 |
| 5 | b | commercialunits_group | 0.0010866078 |
[1] "overall test rmse:"
feature_list = c( "zipcode","commercialunits_group","residentialunits_group","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
)
target = "saleprice_log"
fit_bb = model_xgboost_partial(feature_list,target,chunk_no = 5)
pred_table_bb = fit_bb[[1]]
imp_table_bb = fit_bb[[2]]
imp_table_bb
## target = saleprice
print("overall test rmse:")
calc_rmse(pred_table_bb$pred,pred_table_bb$actual)
calc_rmse(pred_table_bb[actual < 20000000]$pred,pred_table_bb[actual < 20000000]$actual)
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.551880 test-rmse:9.360306 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.731550 test-rmse:6.639933 [3] train-rmse:4.767936 test-rmse:4.660577 [4] train-rmse:3.407883 test-rmse:3.283934 [5] train-rmse:2.469307 test-rmse:2.335088 [6] train-rmse:1.833810 test-rmse:1.696372 [7] train-rmse:1.414465 test-rmse:1.335357 [8] train-rmse:1.148255 test-rmse:1.159151 [9] train-rmse:0.979463 test-rmse:1.038631 [10] train-rmse:0.876598 test-rmse:0.966988 [11] train-rmse:0.814977 test-rmse:0.912099 [12] train-rmse:0.780391 test-rmse:0.935289 [13] train-rmse:0.759587 test-rmse:0.920370 [14] train-rmse:0.744901 test-rmse:0.956010 Stopping. Best iteration: [11] train-rmse:0.814977 test-rmse:0.912099 [1] train-rmse:9.479283 test-rmse:9.568768 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.675124 test-rmse:6.725164 [3] train-rmse:4.722460 test-rmse:4.725270 [4] train-rmse:3.371267 test-rmse:3.350007 [5] train-rmse:2.439072 test-rmse:2.421804 [6] train-rmse:1.804497 test-rmse:1.846933 [7] train-rmse:1.381930 test-rmse:1.502296 [8] train-rmse:1.111757 test-rmse:1.293508 [9] train-rmse:0.944729 test-rmse:1.156977 [10] train-rmse:0.845170 test-rmse:1.094871 [11] train-rmse:0.783811 test-rmse:1.052736 [12] train-rmse:0.747546 test-rmse:1.041908 [13] train-rmse:0.724095 test-rmse:1.035916 [14] train-rmse:0.709452 test-rmse:1.049470 [15] train-rmse:0.701186 test-rmse:1.056146 [16] train-rmse:0.694240 test-rmse:1.065461 Stopping. Best iteration: [13] train-rmse:0.724095 test-rmse:1.035916 [1] train-rmse:9.444277 test-rmse:9.615843 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.645812 test-rmse:6.684451 [3] train-rmse:4.696020 test-rmse:4.668402 [4] train-rmse:3.341680 test-rmse:3.304840 [5] train-rmse:2.409045 test-rmse:2.378281 [6] train-rmse:1.772777 test-rmse:1.785674 [7] train-rmse:1.348280 test-rmse:1.438718 [8] train-rmse:1.070859 test-rmse:1.263081 [9] train-rmse:0.897350 test-rmse:1.191562 [10] train-rmse:0.791352 test-rmse:1.162133 [11] train-rmse:0.729460 test-rmse:1.154931 [12] train-rmse:0.691449 test-rmse:1.155451 [13] train-rmse:0.670332 test-rmse:1.159020 [14] train-rmse:0.655782 test-rmse:1.161739 Stopping. Best iteration: [11] train-rmse:0.729460 test-rmse:1.154931 [1] train-rmse:9.523866 test-rmse:9.457814 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.711334 test-rmse:6.792172 [3] train-rmse:4.753338 test-rmse:4.923649 [4] train-rmse:3.395726 test-rmse:3.624705 [5] train-rmse:2.461407 test-rmse:2.655608 [6] train-rmse:1.831176 test-rmse:2.011463 [7] train-rmse:1.413350 test-rmse:1.568333 [8] train-rmse:1.145884 test-rmse:1.272229 [9] train-rmse:0.978899 test-rmse:1.071148 [10] train-rmse:0.881628 test-rmse:0.952212 [11] train-rmse:0.823119 test-rmse:0.890592 [12] train-rmse:0.790177 test-rmse:0.840979 [13] train-rmse:0.767607 test-rmse:0.811115 [14] train-rmse:0.755994 test-rmse:0.793500 [15] train-rmse:0.747748 test-rmse:0.784291 [16] train-rmse:0.743215 test-rmse:0.775531 [17] train-rmse:0.738901 test-rmse:0.771173 [18] train-rmse:0.733840 test-rmse:0.771211 [19] train-rmse:0.731504 test-rmse:0.769085 [20] train-rmse:0.728834 test-rmse:0.769384 [1] train-rmse:9.512930 test-rmse:9.505802 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.704752 test-rmse:6.727463 [3] train-rmse:4.750471 test-rmse:4.839544 [4] train-rmse:3.397527 test-rmse:3.528449 [5] train-rmse:2.468839 test-rmse:2.653576 [6] train-rmse:1.837183 test-rmse:2.034437 [7] train-rmse:1.423736 test-rmse:1.624240 [8] train-rmse:1.156138 test-rmse:1.343759 [9] train-rmse:0.996589 test-rmse:1.170923 [10] train-rmse:0.896139 test-rmse:1.052877 [11] train-rmse:0.834260 test-rmse:0.996203 [12] train-rmse:0.802487 test-rmse:0.942062 [13] train-rmse:0.780549 test-rmse:0.923245 [14] train-rmse:0.766452 test-rmse:0.907483 [15] train-rmse:0.758289 test-rmse:0.901744 [16] train-rmse:0.748378 test-rmse:0.927343 [17] train-rmse:0.742667 test-rmse:0.925144 [18] train-rmse:0.737387 test-rmse:0.924699 Stopping. Best iteration: [15] train-rmse:0.758289 test-rmse:0.901744
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.309102 test-rmse:9.384217 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.547945 test-rmse:6.616454 [3] train-rmse:4.626319 test-rmse:4.687997 [4] train-rmse:3.294369 test-rmse:3.270407 [5] train-rmse:2.378831 test-rmse:2.307908 [6] train-rmse:1.762135 test-rmse:1.662663 [7] train-rmse:1.358284 test-rmse:1.276278 [8] train-rmse:1.102232 test-rmse:1.075275 [9] train-rmse:0.952034 test-rmse:0.995852 [10] train-rmse:0.866607 test-rmse:1.000012 [11] train-rmse:0.818396 test-rmse:0.989304 [12] train-rmse:0.792146 test-rmse:0.990235 [13] train-rmse:0.779057 test-rmse:1.015369 [14] train-rmse:0.771920 test-rmse:1.038820 Stopping. Best iteration: [11] train-rmse:0.818396 test-rmse:0.989304 [1] train-rmse:9.363312 test-rmse:9.043005 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.585834 test-rmse:6.323466 [3] train-rmse:4.652777 test-rmse:4.429573 [4] train-rmse:3.314602 test-rmse:3.143716 [5] train-rmse:2.395371 test-rmse:2.272855 [6] train-rmse:1.777845 test-rmse:1.677530 [7] train-rmse:1.372414 test-rmse:1.329160 [8] train-rmse:1.119694 test-rmse:1.095091 [9] train-rmse:0.968155 test-rmse:0.969136 [10] train-rmse:0.883116 test-rmse:0.930032 [11] train-rmse:0.836019 test-rmse:0.906378 [12] train-rmse:0.810396 test-rmse:0.900676 [13] train-rmse:0.796645 test-rmse:0.922759 [14] train-rmse:0.789090 test-rmse:0.920758 [15] train-rmse:0.784009 test-rmse:0.924410 Stopping. Best iteration: [12] train-rmse:0.810396 test-rmse:0.900676 [1] train-rmse:9.304173 test-rmse:9.418466 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.542047 test-rmse:6.675941 [3] train-rmse:4.618886 test-rmse:4.772258 [4] train-rmse:3.286777 test-rmse:3.462541 [5] train-rmse:2.372623 test-rmse:2.570891 [6] train-rmse:1.751972 test-rmse:1.983203 [7] train-rmse:1.343042 test-rmse:1.637498 [8] train-rmse:1.084017 test-rmse:1.396231 [9] train-rmse:0.929219 test-rmse:1.251159 [10] train-rmse:0.840249 test-rmse:1.165142 [11] train-rmse:0.791653 test-rmse:1.101120 [12] train-rmse:0.765809 test-rmse:1.064373 [13] train-rmse:0.751198 test-rmse:1.044213 [14] train-rmse:0.743291 test-rmse:1.029532 [15] train-rmse:0.739003 test-rmse:1.019391 [16] train-rmse:0.735785 test-rmse:1.006889 [17] train-rmse:0.734017 test-rmse:1.004799 [18] train-rmse:0.732483 test-rmse:1.001687 [19] train-rmse:0.731497 test-rmse:0.997126 [20] train-rmse:0.730918 test-rmse:0.995573 [1] train-rmse:9.302736 test-rmse:9.428233 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.541879 test-rmse:6.623537 [3] train-rmse:4.620281 test-rmse:4.673359 [4] train-rmse:3.288510 test-rmse:3.418385 [5] train-rmse:2.373392 test-rmse:2.561208 [6] train-rmse:1.754200 test-rmse:1.969724 [7] train-rmse:1.346143 test-rmse:1.606677 [8] train-rmse:1.086728 test-rmse:1.361672 [9] train-rmse:0.931323 test-rmse:1.227243 [10] train-rmse:0.841791 test-rmse:1.156876 [11] train-rmse:0.792188 test-rmse:1.108345 [12] train-rmse:0.765889 test-rmse:1.086065 [13] train-rmse:0.749888 test-rmse:1.073159 [14] train-rmse:0.741330 test-rmse:1.072363 [15] train-rmse:0.736264 test-rmse:1.081399 [16] train-rmse:0.732979 test-rmse:1.079820 [17] train-rmse:0.730042 test-rmse:1.089895 Stopping. Best iteration: [14] train-rmse:0.741330 test-rmse:1.072363 [1] train-rmse:9.316345 test-rmse:9.329835 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.552083 test-rmse:6.551138 [3] train-rmse:4.627890 test-rmse:4.617143 [4] train-rmse:3.295786 test-rmse:3.276470 [5] train-rmse:2.382221 test-rmse:2.358491 [6] train-rmse:1.764711 test-rmse:1.739237 [7] train-rmse:1.359323 test-rmse:1.355024 [8] train-rmse:1.103254 test-rmse:1.123325 [9] train-rmse:0.948415 test-rmse:0.991335 [10] train-rmse:0.860865 test-rmse:0.927174 [11] train-rmse:0.813289 test-rmse:0.891220 [12] train-rmse:0.788102 test-rmse:0.878513 [13] train-rmse:0.772528 test-rmse:0.875732 [14] train-rmse:0.764775 test-rmse:0.866895 [15] train-rmse:0.759869 test-rmse:0.867006 [16] train-rmse:0.756138 test-rmse:0.864977 [17] train-rmse:0.753791 test-rmse:0.870731 [18] train-rmse:0.751765 test-rmse:0.871391 [19] train-rmse:0.751147 test-rmse:0.871597 Stopping. Best iteration: [16] train-rmse:0.756138 test-rmse:0.864977
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.673349 test-rmse:9.797608 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.806695 test-rmse:6.880116 [3] train-rmse:4.809971 test-rmse:4.813921 [4] train-rmse:3.425525 test-rmse:3.402823 [5] train-rmse:2.474746 test-rmse:2.422370 [6] train-rmse:1.831525 test-rmse:1.837925 [7] train-rmse:1.411258 test-rmse:1.491259 [8] train-rmse:1.143227 test-rmse:1.247605 [9] train-rmse:0.984115 test-rmse:1.100770 [10] train-rmse:0.892594 test-rmse:1.028971 [11] train-rmse:0.844031 test-rmse:0.987988 [12] train-rmse:0.818005 test-rmse:0.967809 [13] train-rmse:0.804531 test-rmse:0.957609 [14] train-rmse:0.791329 test-rmse:0.957472 [15] train-rmse:0.787208 test-rmse:0.955146 [16] train-rmse:0.779598 test-rmse:0.955792 [17] train-rmse:0.777976 test-rmse:0.955268 [18] train-rmse:0.777124 test-rmse:0.954691 [19] train-rmse:0.771994 test-rmse:0.945783 [20] train-rmse:0.770204 test-rmse:0.946020 [1] train-rmse:9.719798 test-rmse:9.613984 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.837647 test-rmse:6.691054 [3] train-rmse:4.830659 test-rmse:4.701231 [4] train-rmse:3.438965 test-rmse:3.298182 [5] train-rmse:2.483056 test-rmse:2.345535 [6] train-rmse:1.837130 test-rmse:1.724431 [7] train-rmse:1.415575 test-rmse:1.326449 [8] train-rmse:1.149830 test-rmse:1.119255 [9] train-rmse:0.992325 test-rmse:1.004189 [10] train-rmse:0.901089 test-rmse:0.936074 [11] train-rmse:0.852435 test-rmse:0.907418 [12] train-rmse:0.827485 test-rmse:0.897746 [13] train-rmse:0.814906 test-rmse:0.894822 [14] train-rmse:0.805613 test-rmse:0.888149 [15] train-rmse:0.801310 test-rmse:0.892777 [16] train-rmse:0.792951 test-rmse:0.913536 [17] train-rmse:0.790258 test-rmse:0.908918 Stopping. Best iteration: [14] train-rmse:0.805613 test-rmse:0.888149 [1] train-rmse:9.732181 test-rmse:9.620612 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.845073 test-rmse:6.800437 [3] train-rmse:4.834879 test-rmse:4.821139 [4] train-rmse:3.438321 test-rmse:3.417271 [5] train-rmse:2.477641 test-rmse:2.483657 [6] train-rmse:1.827588 test-rmse:1.841734 [7] train-rmse:1.395997 test-rmse:1.460773 [8] train-rmse:1.125441 test-rmse:1.231500 [9] train-rmse:0.963061 test-rmse:1.104173 [10] train-rmse:0.869802 test-rmse:1.053849 [11] train-rmse:0.819132 test-rmse:1.039933 [12] train-rmse:0.791581 test-rmse:1.023146 [13] train-rmse:0.774971 test-rmse:1.020651 [14] train-rmse:0.767990 test-rmse:1.018474 [15] train-rmse:0.759010 test-rmse:1.041727 [16] train-rmse:0.756189 test-rmse:1.042001 [17] train-rmse:0.750025 test-rmse:1.060336 Stopping. Best iteration: [14] train-rmse:0.767990 test-rmse:1.018474 [1] train-rmse:9.684821 test-rmse:9.884584 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.813963 test-rmse:7.009226 [3] train-rmse:4.815288 test-rmse:5.005859 [4] train-rmse:3.429008 test-rmse:3.593419 [5] train-rmse:2.477298 test-rmse:2.622155 [6] train-rmse:1.833877 test-rmse:1.957792 [7] train-rmse:1.409978 test-rmse:1.513250 [8] train-rmse:1.146691 test-rmse:1.236549 [9] train-rmse:0.990360 test-rmse:1.071826 [10] train-rmse:0.900606 test-rmse:0.973047 [11] train-rmse:0.852974 test-rmse:0.918453 [12] train-rmse:0.824014 test-rmse:0.889140 [13] train-rmse:0.808577 test-rmse:0.872489 [14] train-rmse:0.801169 test-rmse:0.860087 [15] train-rmse:0.794172 test-rmse:0.855601 [16] train-rmse:0.789710 test-rmse:0.850968 [17] train-rmse:0.788136 test-rmse:0.850238 [18] train-rmse:0.786722 test-rmse:0.850586 [19] train-rmse:0.782341 test-rmse:0.850053 [20] train-rmse:0.779757 test-rmse:0.849003 [1] train-rmse:9.731328 test-rmse:9.652334 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.845593 test-rmse:6.870189 [3] train-rmse:4.835332 test-rmse:4.923158 [4] train-rmse:3.441141 test-rmse:3.539722 [5] train-rmse:2.481603 test-rmse:2.587694 [6] train-rmse:1.834113 test-rmse:1.963976 [7] train-rmse:1.409202 test-rmse:1.569064 [8] train-rmse:1.136143 test-rmse:1.273616 [9] train-rmse:0.976368 test-rmse:1.146040 [10] train-rmse:0.885267 test-rmse:1.079106 [11] train-rmse:0.836997 test-rmse:1.043439 [12] train-rmse:0.811836 test-rmse:1.029762 [13] train-rmse:0.795259 test-rmse:1.001793 [14] train-rmse:0.787497 test-rmse:1.028928 [15] train-rmse:0.781337 test-rmse:1.029328 [16] train-rmse:0.778259 test-rmse:1.030060 Stopping. Best iteration: [13] train-rmse:0.795259 test-rmse:1.001793
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:10.388898 test-rmse:11.443415 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.380089 test-rmse:8.185637 [3] train-rmse:5.295609 test-rmse:5.935989 [4] train-rmse:3.870322 test-rmse:4.404634 [5] train-rmse:2.910508 test-rmse:3.367944 [6] train-rmse:2.257802 test-rmse:2.723271 [7] train-rmse:1.826787 test-rmse:2.320602 [8] train-rmse:1.539348 test-rmse:2.103516 [9] train-rmse:1.364664 test-rmse:1.997221 [10] train-rmse:1.251050 test-rmse:1.933154 [11] train-rmse:1.178749 test-rmse:1.916974 [12] train-rmse:1.128995 test-rmse:1.889381 [13] train-rmse:1.097334 test-rmse:1.889008 [14] train-rmse:1.068990 test-rmse:1.880653 [15] train-rmse:1.049461 test-rmse:1.876507 [16] train-rmse:1.039361 test-rmse:1.870676 [17] train-rmse:1.027800 test-rmse:1.878249 [18] train-rmse:1.017979 test-rmse:1.881472 [19] train-rmse:1.006376 test-rmse:1.879905 Stopping. Best iteration: [16] train-rmse:1.039361 test-rmse:1.870676 [1] train-rmse:10.501337 test-rmse:10.932494 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.471530 test-rmse:7.679841 [3] train-rmse:5.377169 test-rmse:5.418252 [4] train-rmse:3.949881 test-rmse:3.842294 [5] train-rmse:2.996875 test-rmse:2.821022 [6] train-rmse:2.353781 test-rmse:2.199211 [7] train-rmse:1.929762 test-rmse:1.760903 [8] train-rmse:1.666386 test-rmse:1.538131 [9] train-rmse:1.492496 test-rmse:1.416916 [10] train-rmse:1.385247 test-rmse:1.408865 [11] train-rmse:1.319285 test-rmse:1.421834 [12] train-rmse:1.275740 test-rmse:1.436734 [13] train-rmse:1.226888 test-rmse:1.434749 Stopping. Best iteration: [10] train-rmse:1.385247 test-rmse:1.408865 [1] train-rmse:10.488774 test-rmse:11.002728 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.460763 test-rmse:7.766029 [3] train-rmse:5.366873 test-rmse:5.518183 [4] train-rmse:3.940761 test-rmse:3.975652 [5] train-rmse:2.986139 test-rmse:2.924240 [6] train-rmse:2.348585 test-rmse:2.262485 [7] train-rmse:1.937843 test-rmse:1.883282 [8] train-rmse:1.668377 test-rmse:1.612242 [9] train-rmse:1.495249 test-rmse:1.493198 [10] train-rmse:1.387844 test-rmse:1.414216 [11] train-rmse:1.299901 test-rmse:1.376623 [12] train-rmse:1.241400 test-rmse:1.381408 [13] train-rmse:1.206649 test-rmse:1.384428 [14] train-rmse:1.184408 test-rmse:1.380645 Stopping. Best iteration: [11] train-rmse:1.299901 test-rmse:1.376623 [1] train-rmse:11.068594 test-rmse:7.299727 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.856607 test-rmse:4.380601 [3] train-rmse:5.619231 test-rmse:3.382090 [4] train-rmse:4.084919 test-rmse:2.883252 [5] train-rmse:3.047599 test-rmse:2.262851 [6] train-rmse:2.338174 test-rmse:2.106817 [7] train-rmse:1.886833 test-rmse:1.994289 [8] train-rmse:1.581040 test-rmse:1.938081 [9] train-rmse:1.391219 test-rmse:1.914735 [10] train-rmse:1.279295 test-rmse:1.908312 [11] train-rmse:1.198684 test-rmse:1.929631 [12] train-rmse:1.130376 test-rmse:1.913679 [13] train-rmse:1.096428 test-rmse:1.918546 Stopping. Best iteration: [10] train-rmse:1.279295 test-rmse:1.908312 [1] train-rmse:10.529497 test-rmse:10.907100 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.488750 test-rmse:7.760101 [3] train-rmse:5.385959 test-rmse:5.573865 [4] train-rmse:3.953602 test-rmse:4.071721 [5] train-rmse:2.992714 test-rmse:3.050246 [6] train-rmse:2.345868 test-rmse:2.384362 [7] train-rmse:1.922814 test-rmse:1.984297 [8] train-rmse:1.649141 test-rmse:1.740893 [9] train-rmse:1.461345 test-rmse:1.632054 [10] train-rmse:1.345373 test-rmse:1.610831 [11] train-rmse:1.262292 test-rmse:1.611614 [12] train-rmse:1.211230 test-rmse:1.648788 [13] train-rmse:1.171450 test-rmse:1.723037 Stopping. Best iteration: [10] train-rmse:1.345373 test-rmse:1.610831
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:10.626890 test-rmse:10.602324 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.541463 test-rmse:7.510668 [3] train-rmse:5.384336 test-rmse:5.344944 [4] train-rmse:3.880072 test-rmse:3.812747 [5] train-rmse:2.846983 test-rmse:2.760466 [6] train-rmse:2.134816 test-rmse:2.041689 [7] train-rmse:1.636790 test-rmse:1.564116 [8] train-rmse:1.323769 test-rmse:1.260990 [9] train-rmse:1.102502 test-rmse:1.059724 [10] train-rmse:0.974419 test-rmse:1.008936 [11] train-rmse:0.887304 test-rmse:0.918896 [12] train-rmse:0.836107 test-rmse:0.868696 [13] train-rmse:0.804454 test-rmse:0.835382 [14] train-rmse:0.787559 test-rmse:0.830906 [15] train-rmse:0.775948 test-rmse:0.826653 [16] train-rmse:0.769098 test-rmse:0.815268 [17] train-rmse:0.764377 test-rmse:0.810871 [18] train-rmse:0.761579 test-rmse:0.807518 [19] train-rmse:0.759573 test-rmse:0.808824 [20] train-rmse:0.758312 test-rmse:0.808671 [1] train-rmse:10.822834 test-rmse:9.225464 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.659511 test-rmse:6.080510 [3] train-rmse:5.440129 test-rmse:3.896571 [4] train-rmse:3.890744 test-rmse:2.421251 [5] train-rmse:2.819366 test-rmse:1.515049 [6] train-rmse:2.086922 test-rmse:1.157604 [7] train-rmse:1.596724 test-rmse:1.137800 [8] train-rmse:1.278828 test-rmse:1.215763 [9] train-rmse:1.041795 test-rmse:1.222994 [10] train-rmse:0.891440 test-rmse:1.239338 Stopping. Best iteration: [7] train-rmse:1.596724 test-rmse:1.137800 [1] train-rmse:10.537583 test-rmse:11.109623 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.470594 test-rmse:8.048811 [3] train-rmse:5.324022 test-rmse:5.908852 [4] train-rmse:3.825353 test-rmse:4.399584 [5] train-rmse:2.781538 test-rmse:3.354000 [6] train-rmse:2.051515 test-rmse:2.607504 [7] train-rmse:1.546627 test-rmse:2.138993 [8] train-rmse:1.182810 test-rmse:1.791448 [9] train-rmse:0.939388 test-rmse:1.573919 [10] train-rmse:0.780712 test-rmse:1.447598 [11] train-rmse:0.673485 test-rmse:1.374165 [12] train-rmse:0.604212 test-rmse:1.334624 [13] train-rmse:0.561432 test-rmse:1.315082 [14] train-rmse:0.532715 test-rmse:1.297797 [15] train-rmse:0.512366 test-rmse:1.288581 [16] train-rmse:0.499621 test-rmse:1.283668 [17] train-rmse:0.492008 test-rmse:1.281680 [18] train-rmse:0.486463 test-rmse:1.281440 [19] train-rmse:0.482397 test-rmse:1.281803 [20] train-rmse:0.480025 test-rmse:1.284475 [1] train-rmse:10.555445 test-rmse:11.039405 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.495431 test-rmse:7.952618 [3] train-rmse:5.358584 test-rmse:5.778204 [4] train-rmse:3.873141 test-rmse:4.223598 [5] train-rmse:2.847970 test-rmse:3.182508 [6] train-rmse:2.133560 test-rmse:2.436529 [7] train-rmse:1.654175 test-rmse:1.883173 [8] train-rmse:1.320973 test-rmse:1.398258 [9] train-rmse:1.103737 test-rmse:1.050800 [10] train-rmse:0.970864 test-rmse:0.855637 [11] train-rmse:0.884744 test-rmse:0.732387 [12] train-rmse:0.833532 test-rmse:0.653307 [13] train-rmse:0.798930 test-rmse:0.623284 [14] train-rmse:0.777492 test-rmse:0.611031 [15] train-rmse:0.762919 test-rmse:0.605076 [16] train-rmse:0.753164 test-rmse:0.605353 [17] train-rmse:0.747548 test-rmse:0.607493 [18] train-rmse:0.742677 test-rmse:0.615682 Stopping. Best iteration: [15] train-rmse:0.762919 test-rmse:0.605076 [1] train-rmse:10.557446 test-rmse:11.027663 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.496887 test-rmse:7.940412 [3] train-rmse:5.359673 test-rmse:5.765745 [4] train-rmse:3.873958 test-rmse:4.211037 [5] train-rmse:2.857905 test-rmse:3.118465 [6] train-rmse:2.158455 test-rmse:2.388045 [7] train-rmse:1.660344 test-rmse:1.718066 [8] train-rmse:1.326615 test-rmse:1.257202 [9] train-rmse:1.108585 test-rmse:0.956360 [10] train-rmse:0.968392 test-rmse:0.813023 [11] train-rmse:0.881682 test-rmse:0.740463 [12] train-rmse:0.826896 test-rmse:0.693680 [13] train-rmse:0.795209 test-rmse:0.669975 [14] train-rmse:0.775916 test-rmse:0.652977 [15] train-rmse:0.764119 test-rmse:0.644548 [16] train-rmse:0.758388 test-rmse:0.639919 [17] train-rmse:0.752934 test-rmse:0.644243 [18] train-rmse:0.748340 test-rmse:0.642636 [19] train-rmse:0.745294 test-rmse:0.641752 Stopping. Best iteration: [16] train-rmse:0.758388 test-rmse:0.639919
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:10.193358 test-rmse:10.722116 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.236953 test-rmse:7.748482 [3] train-rmse:5.169226 test-rmse:5.656140 [4] train-rmse:3.735078 test-rmse:4.187171 [5] train-rmse:2.755675 test-rmse:3.159646 [6] train-rmse:2.077898 test-rmse:2.393152 [7] train-rmse:1.615283 test-rmse:1.857600 [8] train-rmse:1.312534 test-rmse:1.489398 [9] train-rmse:1.114392 test-rmse:1.393675 [10] train-rmse:0.979362 test-rmse:1.333235 [11] train-rmse:0.899189 test-rmse:1.260703 [12] train-rmse:0.835521 test-rmse:1.241317 [13] train-rmse:0.794055 test-rmse:1.168477 [14] train-rmse:0.765281 test-rmse:1.171833 [15] train-rmse:0.746196 test-rmse:1.174588 [16] train-rmse:0.734278 test-rmse:1.152737 [17] train-rmse:0.726442 test-rmse:1.142159 [18] train-rmse:0.721078 test-rmse:1.134043 [19] train-rmse:0.716875 test-rmse:1.139279 [20] train-rmse:0.713966 test-rmse:1.147753 [1] train-rmse:10.370008 test-rmse:9.629025 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.360112 test-rmse:6.600096 [3] train-rmse:5.254796 test-rmse:4.469019 [4] train-rmse:3.794165 test-rmse:2.974826 [5] train-rmse:2.779951 test-rmse:1.945817 [6] train-rmse:2.077941 test-rmse:1.332398 [7] train-rmse:1.601868 test-rmse:0.978186 [8] train-rmse:1.286319 test-rmse:0.825588 [9] train-rmse:1.079102 test-rmse:0.753420 [10] train-rmse:0.946801 test-rmse:0.719563 [11] train-rmse:0.859989 test-rmse:0.659878 [12] train-rmse:0.802917 test-rmse:0.653036 [13] train-rmse:0.762097 test-rmse:0.624042 [14] train-rmse:0.735666 test-rmse:0.604445 [15] train-rmse:0.717055 test-rmse:0.595312 [16] train-rmse:0.705843 test-rmse:0.586715 [17] train-rmse:0.700516 test-rmse:0.577678 [18] train-rmse:0.693929 test-rmse:0.574894 [19] train-rmse:0.689675 test-rmse:0.574024 [20] train-rmse:0.686763 test-rmse:0.574284 [1] train-rmse:10.386724 test-rmse:9.457173 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.354051 test-rmse:6.460222 [3] train-rmse:5.225576 test-rmse:4.388751 [4] train-rmse:3.739174 test-rmse:2.995137 [5] train-rmse:2.703747 test-rmse:2.234076 [6] train-rmse:1.982220 test-rmse:1.761881 [7] train-rmse:1.477590 test-rmse:1.575256 [8] train-rmse:1.133543 test-rmse:1.479614 [9] train-rmse:0.895802 test-rmse:1.436728 [10] train-rmse:0.722780 test-rmse:1.428730 [11] train-rmse:0.602606 test-rmse:1.448051 [12] train-rmse:0.530017 test-rmse:1.472023 [13] train-rmse:0.464567 test-rmse:1.480759 Stopping. Best iteration: [10] train-rmse:0.722780 test-rmse:1.428730 [1] train-rmse:10.150992 test-rmse:10.993076 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.202875 test-rmse:8.028910 [3] train-rmse:5.140057 test-rmse:5.942854 [4] train-rmse:3.707963 test-rmse:4.477384 [5] train-rmse:2.720212 test-rmse:3.346127 [6] train-rmse:2.043510 test-rmse:2.535974 [7] train-rmse:1.593673 test-rmse:1.971554 [8] train-rmse:1.293060 test-rmse:1.537786 [9] train-rmse:1.103955 test-rmse:1.230135 [10] train-rmse:0.972542 test-rmse:1.021376 [11] train-rmse:0.885533 test-rmse:0.861975 [12] train-rmse:0.828536 test-rmse:0.743889 [13] train-rmse:0.790664 test-rmse:0.689249 [14] train-rmse:0.766100 test-rmse:0.652180 [15] train-rmse:0.749331 test-rmse:0.635578 [16] train-rmse:0.737143 test-rmse:0.606735 [17] train-rmse:0.729792 test-rmse:0.591772 [18] train-rmse:0.723880 test-rmse:0.582288 [19] train-rmse:0.720195 test-rmse:0.584046 [20] train-rmse:0.717282 test-rmse:0.581187 [1] train-rmse:10.244374 test-rmse:10.430969 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.270795 test-rmse:7.450532 [3] train-rmse:5.190107 test-rmse:5.359859 [4] train-rmse:3.745719 test-rmse:3.901022 [5] train-rmse:2.741575 test-rmse:2.906604 [6] train-rmse:2.051602 test-rmse:2.311595 [7] train-rmse:1.584475 test-rmse:1.899952 [8] train-rmse:1.277542 test-rmse:1.605616 [9] train-rmse:1.078280 test-rmse:1.323483 [10] train-rmse:0.948748 test-rmse:1.177981 [11] train-rmse:0.866816 test-rmse:1.077412 [12] train-rmse:0.812614 test-rmse:1.025443 [13] train-rmse:0.778351 test-rmse:0.967482 [14] train-rmse:0.755974 test-rmse:0.942737 [15] train-rmse:0.742663 test-rmse:0.928786 [16] train-rmse:0.734877 test-rmse:0.921980 [17] train-rmse:0.729771 test-rmse:0.913213 [18] train-rmse:0.726848 test-rmse:0.910934 [19] train-rmse:0.724246 test-rmse:0.907773 [20] train-rmse:0.722631 test-rmse:0.904065
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.021288 test-rmse:9.080522 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.359286 test-rmse:6.418517 [3] train-rmse:4.500711 test-rmse:4.570905 [4] train-rmse:3.209853 test-rmse:3.286716 [5] train-rmse:2.321106 test-rmse:2.383809 [6] train-rmse:1.718175 test-rmse:1.764616 [7] train-rmse:1.316232 test-rmse:1.393778 [8] train-rmse:1.060911 test-rmse:1.147616 [9] train-rmse:0.904496 test-rmse:0.986216 [10] train-rmse:0.810071 test-rmse:0.908097 [11] train-rmse:0.746498 test-rmse:0.878104 [12] train-rmse:0.710640 test-rmse:0.841751 [13] train-rmse:0.688118 test-rmse:0.812458 [14] train-rmse:0.676119 test-rmse:0.793893 [15] train-rmse:0.667153 test-rmse:0.785138 [16] train-rmse:0.663139 test-rmse:0.774644 [17] train-rmse:0.657734 test-rmse:0.761490 [18] train-rmse:0.653759 test-rmse:0.759695 [19] train-rmse:0.650641 test-rmse:0.752154 [20] train-rmse:0.648096 test-rmse:0.750070 [1] train-rmse:9.028586 test-rmse:9.069464 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.364664 test-rmse:6.429575 [3] train-rmse:4.501265 test-rmse:4.545743 [4] train-rmse:3.206617 test-rmse:3.242751 [5] train-rmse:2.312769 test-rmse:2.356140 [6] train-rmse:1.702256 test-rmse:1.759251 [7] train-rmse:1.297004 test-rmse:1.391879 [8] train-rmse:1.032597 test-rmse:1.184146 [9] train-rmse:0.870422 test-rmse:1.075070 [10] train-rmse:0.773286 test-rmse:1.009911 [11] train-rmse:0.709873 test-rmse:0.982635 [12] train-rmse:0.677393 test-rmse:0.978328 [13] train-rmse:0.656878 test-rmse:0.978715 [14] train-rmse:0.642832 test-rmse:0.997931 [15] train-rmse:0.632896 test-rmse:1.006231 Stopping. Best iteration: [12] train-rmse:0.677393 test-rmse:0.978328 [1] train-rmse:9.016145 test-rmse:9.098762 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.355142 test-rmse:6.402873 [3] train-rmse:4.500142 test-rmse:4.539625 [4] train-rmse:3.208925 test-rmse:3.249192 [5] train-rmse:2.316380 test-rmse:2.356167 [6] train-rmse:1.709896 test-rmse:1.737036 [7] train-rmse:1.309502 test-rmse:1.323077 [8] train-rmse:1.051956 test-rmse:1.073583 [9] train-rmse:0.886008 test-rmse:0.947853 [10] train-rmse:0.788885 test-rmse:0.879115 [11] train-rmse:0.731889 test-rmse:0.847081 [12] train-rmse:0.698614 test-rmse:0.837684 [13] train-rmse:0.679141 test-rmse:0.833829 [14] train-rmse:0.666583 test-rmse:0.837444 [15] train-rmse:0.658788 test-rmse:0.835066 [16] train-rmse:0.654671 test-rmse:0.835004 Stopping. Best iteration: [13] train-rmse:0.679141 test-rmse:0.833829 [1] train-rmse:9.036717 test-rmse:8.974507 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.370030 test-rmse:6.340525 [3] train-rmse:4.512846 test-rmse:4.509331 [4] train-rmse:3.217882 test-rmse:3.234575 [5] train-rmse:2.326023 test-rmse:2.355937 [6] train-rmse:1.719232 test-rmse:1.767543 [7] train-rmse:1.315831 test-rmse:1.372884 [8] train-rmse:1.056124 test-rmse:1.138313 [9] train-rmse:0.894665 test-rmse:0.970762 [10] train-rmse:0.789563 test-rmse:0.877331 [11] train-rmse:0.728259 test-rmse:0.827671 [12] train-rmse:0.696673 test-rmse:0.790909 [13] train-rmse:0.677768 test-rmse:0.774501 [14] train-rmse:0.666825 test-rmse:0.760483 [15] train-rmse:0.657877 test-rmse:0.754788 [16] train-rmse:0.652543 test-rmse:0.753649 [17] train-rmse:0.645198 test-rmse:0.747503 [18] train-rmse:0.638689 test-rmse:0.750902 [19] train-rmse:0.636623 test-rmse:0.750427 [20] train-rmse:0.635293 test-rmse:0.748796 Stopping. Best iteration: [17] train-rmse:0.645198 test-rmse:0.747503 [1] train-rmse:9.039584 test-rmse:8.868618 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.370659 test-rmse:6.217652 [3] train-rmse:4.507889 test-rmse:4.369915 [4] train-rmse:3.211636 test-rmse:3.083428 [5] train-rmse:2.318417 test-rmse:2.205442 [6] train-rmse:1.711186 test-rmse:1.617296 [7] train-rmse:1.314653 test-rmse:1.236292 [8] train-rmse:1.046473 test-rmse:1.001685 [9] train-rmse:0.885313 test-rmse:0.860079 [10] train-rmse:0.784226 test-rmse:0.795239 [11] train-rmse:0.718275 test-rmse:0.773717 [12] train-rmse:0.679510 test-rmse:0.763279 [13] train-rmse:0.659863 test-rmse:0.766164 [14] train-rmse:0.645271 test-rmse:0.770272 [15] train-rmse:0.636548 test-rmse:0.771628 Stopping. Best iteration: [12] train-rmse:0.679510 test-rmse:0.763279
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.335924 test-rmse:7.926149 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.865353 test-rmse:5.430678 [3] train-rmse:4.140767 test-rmse:3.794957 [4] train-rmse:2.939697 test-rmse:2.656463 [5] train-rmse:2.109566 test-rmse:1.887636 [6] train-rmse:1.545533 test-rmse:1.370913 [7] train-rmse:1.158444 test-rmse:1.083726 [8] train-rmse:0.908885 test-rmse:0.931598 [9] train-rmse:0.747630 test-rmse:0.906228 [10] train-rmse:0.652592 test-rmse:0.905269 [11] train-rmse:0.597170 test-rmse:0.920902 [12] train-rmse:0.567487 test-rmse:0.925148 [13] train-rmse:0.549714 test-rmse:0.947330 Stopping. Best iteration: [10] train-rmse:0.652592 test-rmse:0.905269 [1] train-rmse:8.262834 test-rmse:8.409632 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.818298 test-rmse:5.920496 [3] train-rmse:4.112905 test-rmse:4.285977 [4] train-rmse:2.926822 test-rmse:3.088020 [5] train-rmse:2.107578 test-rmse:2.307869 [6] train-rmse:1.549914 test-rmse:1.797094 [7] train-rmse:1.167338 test-rmse:1.519996 [8] train-rmse:0.917661 test-rmse:1.359728 [9] train-rmse:0.760295 test-rmse:1.263506 [10] train-rmse:0.667964 test-rmse:1.183715 [11] train-rmse:0.609749 test-rmse:1.161232 [12] train-rmse:0.580779 test-rmse:1.128297 [13] train-rmse:0.564648 test-rmse:1.108756 [14] train-rmse:0.551681 test-rmse:1.117150 [15] train-rmse:0.545925 test-rmse:1.122503 [16] train-rmse:0.540857 test-rmse:1.118824 Stopping. Best iteration: [13] train-rmse:0.564648 test-rmse:1.108756 [1] train-rmse:8.287754 test-rmse:8.344789 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.835353 test-rmse:5.858355 [3] train-rmse:4.125087 test-rmse:4.124052 [4] train-rmse:2.938517 test-rmse:2.919716 [5] train-rmse:2.117116 test-rmse:2.090832 [6] train-rmse:1.563087 test-rmse:1.539766 [7] train-rmse:1.184842 test-rmse:1.167971 [8] train-rmse:0.934306 test-rmse:0.925627 [9] train-rmse:0.780733 test-rmse:0.784820 [10] train-rmse:0.694392 test-rmse:0.710769 [11] train-rmse:0.638210 test-rmse:0.672246 [12] train-rmse:0.603621 test-rmse:0.652942 [13] train-rmse:0.588505 test-rmse:0.645344 [14] train-rmse:0.574524 test-rmse:0.642816 [15] train-rmse:0.569684 test-rmse:0.642310 [16] train-rmse:0.562428 test-rmse:0.644729 [17] train-rmse:0.560439 test-rmse:0.644965 [18] train-rmse:0.554985 test-rmse:0.644373 Stopping. Best iteration: [15] train-rmse:0.569684 test-rmse:0.642310 [1] train-rmse:8.253007 test-rmse:8.540234 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.810067 test-rmse:6.065097 [3] train-rmse:4.105932 test-rmse:4.339065 [4] train-rmse:2.923019 test-rmse:3.140804 [5] train-rmse:2.108961 test-rmse:2.314381 [6] train-rmse:1.555921 test-rmse:1.725479 [7] train-rmse:1.178889 test-rmse:1.324195 [8] train-rmse:0.932429 test-rmse:1.042424 [9] train-rmse:0.781568 test-rmse:0.859395 [10] train-rmse:0.696307 test-rmse:0.759310 [11] train-rmse:0.640592 test-rmse:0.694642 [12] train-rmse:0.609142 test-rmse:0.658336 [13] train-rmse:0.590685 test-rmse:0.628487 [14] train-rmse:0.577271 test-rmse:0.614818 [15] train-rmse:0.572460 test-rmse:0.606686 [16] train-rmse:0.569601 test-rmse:0.602031 [17] train-rmse:0.566550 test-rmse:0.602880 [18] train-rmse:0.563788 test-rmse:0.600644 [19] train-rmse:0.561961 test-rmse:0.598305 [20] train-rmse:0.560639 test-rmse:0.597332 [1] train-rmse:8.297146 test-rmse:8.228351 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.840725 test-rmse:5.821993 [3] train-rmse:4.127257 test-rmse:4.142850 [4] train-rmse:2.935636 test-rmse:2.956568 [5] train-rmse:2.114021 test-rmse:2.134679 [6] train-rmse:1.550408 test-rmse:1.607225 [7] train-rmse:1.170539 test-rmse:1.241454 [8] train-rmse:0.919207 test-rmse:0.997897 [9] train-rmse:0.759744 test-rmse:0.863502 [10] train-rmse:0.662509 test-rmse:0.789627 [11] train-rmse:0.608919 test-rmse:0.751155 [12] train-rmse:0.577883 test-rmse:0.729607 [13] train-rmse:0.561130 test-rmse:0.731290 [14] train-rmse:0.550291 test-rmse:0.722446 [15] train-rmse:0.544164 test-rmse:0.716747 [16] train-rmse:0.540764 test-rmse:0.712449 [17] train-rmse:0.538217 test-rmse:0.718194 [18] train-rmse:0.536810 test-rmse:0.729523 [19] train-rmse:0.535327 test-rmse:0.736381 Stopping. Best iteration: [16] train-rmse:0.540764 test-rmse:0.712449
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.161335 test-rmse:8.620878 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.762904 test-rmse:6.219589 [3] train-rmse:4.094057 test-rmse:4.461231 [4] train-rmse:2.942621 test-rmse:3.249793 [5] train-rmse:2.149594 test-rmse:2.470204 [6] train-rmse:1.615821 test-rmse:1.938085 [7] train-rmse:1.250596 test-rmse:1.545808 [8] train-rmse:1.025304 test-rmse:1.302690 [9] train-rmse:0.889713 test-rmse:1.176568 [10] train-rmse:0.798507 test-rmse:1.164954 [11] train-rmse:0.743876 test-rmse:1.184327 [12] train-rmse:0.712051 test-rmse:1.226648 [13] train-rmse:0.693870 test-rmse:1.270048 Stopping. Best iteration: [10] train-rmse:0.798507 test-rmse:1.164954 [1] train-rmse:8.281688 test-rmse:7.897488 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.850813 test-rmse:5.458279 [3] train-rmse:4.161161 test-rmse:3.875648 [4] train-rmse:2.997547 test-rmse:2.782690 [5] train-rmse:2.194860 test-rmse:2.047831 [6] train-rmse:1.644788 test-rmse:1.542443 [7] train-rmse:1.289073 test-rmse:1.215306 [8] train-rmse:1.035661 test-rmse:1.004550 [9] train-rmse:0.870927 test-rmse:0.894784 [10] train-rmse:0.770193 test-rmse:0.829173 [11] train-rmse:0.706923 test-rmse:0.794510 [12] train-rmse:0.669072 test-rmse:0.766450 [13] train-rmse:0.645850 test-rmse:0.748711 [14] train-rmse:0.631643 test-rmse:0.738571 [15] train-rmse:0.622905 test-rmse:0.733879 [16] train-rmse:0.617256 test-rmse:0.731191 [17] train-rmse:0.613332 test-rmse:0.729639 [18] train-rmse:0.610783 test-rmse:0.728991 [19] train-rmse:0.609117 test-rmse:0.728523 [20] train-rmse:0.608084 test-rmse:0.728067 [1] train-rmse:8.307909 test-rmse:7.737664 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.870535 test-rmse:5.284632 [3] train-rmse:4.178356 test-rmse:3.680520 [4] train-rmse:3.011836 test-rmse:2.588568 [5] train-rmse:2.206296 test-rmse:1.862271 [6] train-rmse:1.654845 test-rmse:1.363816 [7] train-rmse:1.281875 test-rmse:1.057343 [8] train-rmse:1.031450 test-rmse:0.863735 [9] train-rmse:0.868452 test-rmse:0.755283 [10] train-rmse:0.767823 test-rmse:0.699322 [11] train-rmse:0.707527 test-rmse:0.670793 [12] train-rmse:0.671247 test-rmse:0.656866 [13] train-rmse:0.648549 test-rmse:0.650329 [14] train-rmse:0.636136 test-rmse:0.646769 [15] train-rmse:0.628206 test-rmse:0.645021 [16] train-rmse:0.621637 test-rmse:0.644955 [17] train-rmse:0.617346 test-rmse:0.645100 [18] train-rmse:0.614040 test-rmse:0.645286 [19] train-rmse:0.611999 test-rmse:0.645838 Stopping. Best iteration: [16] train-rmse:0.621637 test-rmse:0.644955 [1] train-rmse:8.265307 test-rmse:8.004170 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.839926 test-rmse:5.569800 [3] train-rmse:4.156545 test-rmse:3.920824 [4] train-rmse:2.995991 test-rmse:2.791524 [5] train-rmse:2.182354 test-rmse:1.994592 [6] train-rmse:1.633764 test-rmse:1.434276 [7] train-rmse:1.271544 test-rmse:1.076638 [8] train-rmse:1.022872 test-rmse:0.849062 [9] train-rmse:0.865576 test-rmse:0.737461 [10] train-rmse:0.768882 test-rmse:0.681608 [11] train-rmse:0.705826 test-rmse:0.666183 [12] train-rmse:0.666491 test-rmse:0.665308 [13] train-rmse:0.644529 test-rmse:0.678499 [14] train-rmse:0.629576 test-rmse:0.696614 [15] train-rmse:0.619286 test-rmse:0.715516 Stopping. Best iteration: [12] train-rmse:0.666491 test-rmse:0.665308 [1] train-rmse:8.121186 test-rmse:8.826150 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.726180 test-rmse:6.445007 [3] train-rmse:4.059022 test-rmse:4.689852 [4] train-rmse:2.900095 test-rmse:3.451998 [5] train-rmse:2.097722 test-rmse:2.681716 [6] train-rmse:1.548828 test-rmse:2.202544 [7] train-rmse:1.181873 test-rmse:1.934001 [8] train-rmse:0.941164 test-rmse:1.755271 [9] train-rmse:0.790238 test-rmse:1.642020 [10] train-rmse:0.698700 test-rmse:1.590436 [11] train-rmse:0.646345 test-rmse:1.547361 [12] train-rmse:0.615294 test-rmse:1.534749 [13] train-rmse:0.597865 test-rmse:1.526549 [14] train-rmse:0.588181 test-rmse:1.523800 [15] train-rmse:0.582786 test-rmse:1.521078 [16] train-rmse:0.579598 test-rmse:1.516573 [17] train-rmse:0.577790 test-rmse:1.517912 [18] train-rmse:0.576695 test-rmse:1.520653 [19] train-rmse:0.576055 test-rmse:1.524066 Stopping. Best iteration: [16] train-rmse:0.579598 test-rmse:1.516573
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.317825 test-rmse:9.256829 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.613880 test-rmse:6.566730 [3] train-rmse:4.739000 test-rmse:4.751905 [4] train-rmse:3.455736 test-rmse:3.527051 [5] train-rmse:2.573594 test-rmse:2.745652 [6] train-rmse:1.981211 test-rmse:2.235021 [7] train-rmse:1.599507 test-rmse:1.942675 [8] train-rmse:1.348593 test-rmse:1.768627 [9] train-rmse:1.190990 test-rmse:1.644398 [10] train-rmse:1.092999 test-rmse:1.574854 [11] train-rmse:1.026490 test-rmse:1.545860 [12] train-rmse:0.975913 test-rmse:1.543410 [13] train-rmse:0.943763 test-rmse:1.540541 [14] train-rmse:0.920663 test-rmse:1.537985 [15] train-rmse:0.900311 test-rmse:1.536796 [16] train-rmse:0.887343 test-rmse:1.542820 [17] train-rmse:0.876574 test-rmse:1.540435 [18] train-rmse:0.867828 test-rmse:1.539952 Stopping. Best iteration: [15] train-rmse:0.900311 test-rmse:1.536796 [1] train-rmse:9.294096 test-rmse:9.429433 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.600555 test-rmse:6.715519 [3] train-rmse:4.735932 test-rmse:4.839403 [4] train-rmse:3.452892 test-rmse:3.772891 [5] train-rmse:2.585396 test-rmse:3.104255 [6] train-rmse:2.010370 test-rmse:2.554428 [7] train-rmse:1.630345 test-rmse:2.156983 [8] train-rmse:1.394993 test-rmse:1.906272 [9] train-rmse:1.256083 test-rmse:1.762341 [10] train-rmse:1.162671 test-rmse:1.651713 [11] train-rmse:1.104769 test-rmse:1.584866 [12] train-rmse:1.069221 test-rmse:1.560656 [13] train-rmse:1.049307 test-rmse:1.546617 [14] train-rmse:1.030513 test-rmse:1.541400 [15] train-rmse:1.012787 test-rmse:1.536202 [16] train-rmse:1.001705 test-rmse:1.537639 [17] train-rmse:0.981036 test-rmse:1.548451 [18] train-rmse:0.967071 test-rmse:1.547932 Stopping. Best iteration: [15] train-rmse:1.012787 test-rmse:1.536202 [1] train-rmse:9.337553 test-rmse:9.161744 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.632817 test-rmse:6.503335 [3] train-rmse:4.764330 test-rmse:4.687876 [4] train-rmse:3.481060 test-rmse:3.445119 [5] train-rmse:2.614452 test-rmse:2.522720 [6] train-rmse:2.035072 test-rmse:1.923426 [7] train-rmse:1.652842 test-rmse:1.558129 [8] train-rmse:1.398658 test-rmse:1.349890 [9] train-rmse:1.256893 test-rmse:1.238104 [10] train-rmse:1.174575 test-rmse:1.207404 [11] train-rmse:1.125688 test-rmse:1.238947 [12] train-rmse:1.089842 test-rmse:1.264485 [13] train-rmse:1.070047 test-rmse:1.300759 Stopping. Best iteration: [10] train-rmse:1.174575 test-rmse:1.207404 [1] train-rmse:9.221989 test-rmse:9.866948 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.556814 test-rmse:7.089923 [3] train-rmse:4.715789 test-rmse:5.175927 [4] train-rmse:3.456151 test-rmse:3.827883 [5] train-rmse:2.593372 test-rmse:2.919463 [6] train-rmse:2.025817 test-rmse:2.308950 [7] train-rmse:1.639650 test-rmse:1.832570 [8] train-rmse:1.394971 test-rmse:1.565428 [9] train-rmse:1.250611 test-rmse:1.389681 [10] train-rmse:1.141589 test-rmse:1.299725 [11] train-rmse:1.073226 test-rmse:1.273280 [12] train-rmse:1.036621 test-rmse:1.242676 [13] train-rmse:1.006334 test-rmse:1.216074 [14] train-rmse:0.986563 test-rmse:1.194375 [15] train-rmse:0.968572 test-rmse:1.201126 [16] train-rmse:0.952423 test-rmse:1.200768 [17] train-rmse:0.943277 test-rmse:1.204803 Stopping. Best iteration: [14] train-rmse:0.986563 test-rmse:1.194375 [1] train-rmse:9.386800 test-rmse:8.802052 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.655944 test-rmse:6.124847 [3] train-rmse:4.763472 test-rmse:4.266863 [4] train-rmse:3.463232 test-rmse:3.037783 [5] train-rmse:2.582692 test-rmse:2.314216 [6] train-rmse:1.988724 test-rmse:1.912257 [7] train-rmse:1.592144 test-rmse:1.730249 [8] train-rmse:1.346800 test-rmse:1.639508 [9] train-rmse:1.178096 test-rmse:1.614394 [10] train-rmse:1.068341 test-rmse:1.640396 [11] train-rmse:0.989988 test-rmse:1.663833 [12] train-rmse:0.943553 test-rmse:1.694786 Stopping. Best iteration: [9] train-rmse:1.178096 test-rmse:1.614394
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.709073 test-rmse:8.368836 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.119796 test-rmse:5.790775 [3] train-rmse:4.314829 test-rmse:4.002976 [4] train-rmse:3.061286 test-rmse:2.798277 [5] train-rmse:2.196244 test-rmse:1.983177 [6] train-rmse:1.607498 test-rmse:1.466641 [7] train-rmse:1.216529 test-rmse:1.164305 [8] train-rmse:0.963890 test-rmse:1.012580 [9] train-rmse:0.808067 test-rmse:0.948699 [10] train-rmse:0.713642 test-rmse:0.931671 [11] train-rmse:0.660761 test-rmse:0.935780 [12] train-rmse:0.631766 test-rmse:0.947680 [13] train-rmse:0.617420 test-rmse:0.960650 Stopping. Best iteration: [10] train-rmse:0.713642 test-rmse:0.931671 [1] train-rmse:8.635538 test-rmse:8.835475 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.072495 test-rmse:6.270820 [3] train-rmse:4.287634 test-rmse:4.483283 [4] train-rmse:3.051362 test-rmse:3.230019 [5] train-rmse:2.202712 test-rmse:2.364071 [6] train-rmse:1.629889 test-rmse:1.761166 [7] train-rmse:1.255137 test-rmse:1.361822 [8] train-rmse:1.019422 test-rmse:1.080319 [9] train-rmse:0.877635 test-rmse:0.918840 [10] train-rmse:0.795795 test-rmse:0.806072 [11] train-rmse:0.751706 test-rmse:0.746001 [12] train-rmse:0.728545 test-rmse:0.712054 [13] train-rmse:0.714795 test-rmse:0.693076 [14] train-rmse:0.708329 test-rmse:0.681465 [15] train-rmse:0.704062 test-rmse:0.675228 [16] train-rmse:0.702181 test-rmse:0.669832 [17] train-rmse:0.700847 test-rmse:0.667520 [18] train-rmse:0.700242 test-rmse:0.665533 [19] train-rmse:0.699355 test-rmse:0.664193 [20] train-rmse:0.698969 test-rmse:0.665662 [1] train-rmse:8.639737 test-rmse:8.816360 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.077857 test-rmse:6.246154 [3] train-rmse:4.293119 test-rmse:4.440389 [4] train-rmse:3.056038 test-rmse:3.185210 [5] train-rmse:2.206799 test-rmse:2.324503 [6] train-rmse:1.633223 test-rmse:1.729487 [7] train-rmse:1.257054 test-rmse:1.345119 [8] train-rmse:1.021456 test-rmse:1.094230 [9] train-rmse:0.880567 test-rmse:0.932655 [10] train-rmse:0.800397 test-rmse:0.844845 [11] train-rmse:0.754966 test-rmse:0.788271 [12] train-rmse:0.731036 test-rmse:0.748031 [13] train-rmse:0.718861 test-rmse:0.725753 [14] train-rmse:0.712774 test-rmse:0.711322 [15] train-rmse:0.708859 test-rmse:0.701484 [16] train-rmse:0.706757 test-rmse:0.695694 [17] train-rmse:0.705691 test-rmse:0.692426 [18] train-rmse:0.704920 test-rmse:0.687725 [19] train-rmse:0.704374 test-rmse:0.684661 [20] train-rmse:0.704059 test-rmse:0.683711 [1] train-rmse:8.649302 test-rmse:8.755192 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.083168 test-rmse:6.185230 [3] train-rmse:4.296566 test-rmse:4.393044 [4] train-rmse:3.060068 test-rmse:3.148420 [5] train-rmse:2.212420 test-rmse:2.280734 [6] train-rmse:1.641550 test-rmse:1.652673 [7] train-rmse:1.268151 test-rmse:1.228474 [8] train-rmse:1.032375 test-rmse:0.943540 [9] train-rmse:0.890999 test-rmse:0.761456 [10] train-rmse:0.810844 test-rmse:0.658795 [11] train-rmse:0.765040 test-rmse:0.606055 [12] train-rmse:0.741168 test-rmse:0.588444 [13] train-rmse:0.728381 test-rmse:0.585503 [14] train-rmse:0.719802 test-rmse:0.585063 [15] train-rmse:0.715513 test-rmse:0.588769 [16] train-rmse:0.712932 test-rmse:0.593567 [17] train-rmse:0.711504 test-rmse:0.597890 Stopping. Best iteration: [14] train-rmse:0.719802 test-rmse:0.585063 [1] train-rmse:8.684432 test-rmse:8.535817 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.106586 test-rmse:5.958110 [3] train-rmse:4.311294 test-rmse:4.163383 [4] train-rmse:3.066265 test-rmse:2.945004 [5] train-rmse:2.210161 test-rmse:2.104827 [6] train-rmse:1.631214 test-rmse:1.546032 [7] train-rmse:1.247648 test-rmse:1.216102 [8] train-rmse:1.004322 test-rmse:1.012347 [9] train-rmse:0.858410 test-rmse:0.891641 [10] train-rmse:0.773708 test-rmse:0.836170 [11] train-rmse:0.726732 test-rmse:0.794832 [12] train-rmse:0.702009 test-rmse:0.770231 [13] train-rmse:0.689446 test-rmse:0.759360 [14] train-rmse:0.680433 test-rmse:0.752871 [15] train-rmse:0.676339 test-rmse:0.748700 [16] train-rmse:0.674327 test-rmse:0.747101 [17] train-rmse:0.672785 test-rmse:0.745503 [18] train-rmse:0.670638 test-rmse:0.744485 [19] train-rmse:0.670010 test-rmse:0.744156 [20] train-rmse:0.669549 test-rmse:0.744096
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.750348 test-rmse:8.712265 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.154364 test-rmse:6.119518 [3] train-rmse:4.347582 test-rmse:4.317407 [4] train-rmse:3.097787 test-rmse:3.074284 [5] train-rmse:2.243442 test-rmse:2.229235 [6] train-rmse:1.671890 test-rmse:1.669958 [7] train-rmse:1.301109 test-rmse:1.318222 [8] train-rmse:1.069640 test-rmse:1.109752 [9] train-rmse:0.933131 test-rmse:0.994724 [10] train-rmse:0.856685 test-rmse:0.934965 [11] train-rmse:0.815808 test-rmse:0.903670 [12] train-rmse:0.794633 test-rmse:0.888242 [13] train-rmse:0.783865 test-rmse:0.881756 [14] train-rmse:0.777776 test-rmse:0.877737 [15] train-rmse:0.774898 test-rmse:0.876854 [16] train-rmse:0.773373 test-rmse:0.876745 [17] train-rmse:0.772288 test-rmse:0.875787 [18] train-rmse:0.771811 test-rmse:0.876008 [19] train-rmse:0.771590 test-rmse:0.875945 [20] train-rmse:0.771443 test-rmse:0.875841 Stopping. Best iteration: [17] train-rmse:0.772288 test-rmse:0.875787 [1] train-rmse:8.741891 test-rmse:8.768330 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.149892 test-rmse:6.175094 [3] train-rmse:4.346498 test-rmse:4.369892 [4] train-rmse:3.099874 test-rmse:3.120645 [5] train-rmse:2.248765 test-rmse:2.265795 [6] train-rmse:1.680628 test-rmse:1.692559 [7] train-rmse:1.313685 test-rmse:1.322886 [8] train-rmse:1.086728 test-rmse:1.088041 [9] train-rmse:0.953677 test-rmse:0.950722 [10] train-rmse:0.880350 test-rmse:0.870994 [11] train-rmse:0.841091 test-rmse:0.834027 [12] train-rmse:0.820407 test-rmse:0.817942 [13] train-rmse:0.810065 test-rmse:0.811863 [14] train-rmse:0.804348 test-rmse:0.810196 [15] train-rmse:0.801661 test-rmse:0.812210 [16] train-rmse:0.800270 test-rmse:0.813757 [17] train-rmse:0.799341 test-rmse:0.815393 Stopping. Best iteration: [14] train-rmse:0.804348 test-rmse:0.810196 [1] train-rmse:8.724227 test-rmse:8.883833 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.140309 test-rmse:6.289765 [3] train-rmse:4.343709 test-rmse:4.478667 [4] train-rmse:3.103364 test-rmse:3.217818 [5] train-rmse:2.258558 test-rmse:2.344660 [6] train-rmse:1.696963 test-rmse:1.745547 [7] train-rmse:1.334303 test-rmse:1.337830 [8] train-rmse:1.114060 test-rmse:1.065674 [9] train-rmse:0.985391 test-rmse:0.899970 [10] train-rmse:0.914885 test-rmse:0.799217 [11] train-rmse:0.877754 test-rmse:0.728647 [12] train-rmse:0.856456 test-rmse:0.689025 [13] train-rmse:0.846532 test-rmse:0.666598 [14] train-rmse:0.840974 test-rmse:0.650284 [15] train-rmse:0.837695 test-rmse:0.641541 [16] train-rmse:0.835992 test-rmse:0.636478 [17] train-rmse:0.835197 test-rmse:0.632367 [18] train-rmse:0.834501 test-rmse:0.632236 [19] train-rmse:0.834166 test-rmse:0.630295 [20] train-rmse:0.833918 test-rmse:0.628875 [1] train-rmse:8.751890 test-rmse:8.700716 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.154809 test-rmse:6.108783 [3] train-rmse:4.347003 test-rmse:4.308384 [4] train-rmse:3.096141 test-rmse:3.068066 [5] train-rmse:2.240604 test-rmse:2.227135 [6] train-rmse:1.667585 test-rmse:1.674636 [7] train-rmse:1.295221 test-rmse:1.306457 [8] train-rmse:1.061423 test-rmse:1.098041 [9] train-rmse:0.924588 test-rmse:0.984506 [10] train-rmse:0.847974 test-rmse:0.930193 [11] train-rmse:0.807408 test-rmse:0.909565 [12] train-rmse:0.784747 test-rmse:0.903160 [13] train-rmse:0.773113 test-rmse:0.902795 [14] train-rmse:0.766676 test-rmse:0.904208 [15] train-rmse:0.762450 test-rmse:0.905852 [16] train-rmse:0.760012 test-rmse:0.909537 Stopping. Best iteration: [13] train-rmse:0.773113 test-rmse:0.902795 [1] train-rmse:8.758749 test-rmse:8.660749 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.160434 test-rmse:6.065176 [3] train-rmse:4.352100 test-rmse:4.260998 [4] train-rmse:3.101325 test-rmse:3.016552 [5] train-rmse:2.246430 test-rmse:2.171202 [6] train-rmse:1.674652 test-rmse:1.613258 [7] train-rmse:1.304587 test-rmse:1.249545 [8] train-rmse:1.073131 test-rmse:1.043149 [9] train-rmse:0.935842 test-rmse:0.943980 [10] train-rmse:0.859771 test-rmse:0.892852 [11] train-rmse:0.819229 test-rmse:0.870474 [12] train-rmse:0.798253 test-rmse:0.860743 [13] train-rmse:0.787497 test-rmse:0.857568 [14] train-rmse:0.781017 test-rmse:0.856253 [15] train-rmse:0.777531 test-rmse:0.856133 [16] train-rmse:0.775056 test-rmse:0.857149 [17] train-rmse:0.774040 test-rmse:0.857509 [18] train-rmse:0.773496 test-rmse:0.857726 Stopping. Best iteration: [15] train-rmse:0.777531 test-rmse:0.856133
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.301628 test-rmse:9.409632 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.556760 test-rmse:6.652610 [3] train-rmse:4.648014 test-rmse:4.712819 [4] train-rmse:3.329327 test-rmse:3.374838 [5] train-rmse:2.429952 test-rmse:2.450481 [6] train-rmse:1.818318 test-rmse:1.849031 [7] train-rmse:1.423854 test-rmse:1.451507 [8] train-rmse:1.176897 test-rmse:1.214805 [9] train-rmse:1.024745 test-rmse:1.088569 [10] train-rmse:0.934497 test-rmse:1.017652 [11] train-rmse:0.882641 test-rmse:0.978104 [12] train-rmse:0.851568 test-rmse:0.968193 [13] train-rmse:0.832795 test-rmse:0.955772 [14] train-rmse:0.820616 test-rmse:0.953853 [15] train-rmse:0.813752 test-rmse:0.948950 [16] train-rmse:0.805265 test-rmse:0.957540 [17] train-rmse:0.797854 test-rmse:0.968681 [18] train-rmse:0.794596 test-rmse:0.965086 Stopping. Best iteration: [15] train-rmse:0.813752 test-rmse:0.948950 [1] train-rmse:9.303020 test-rmse:9.396348 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.555760 test-rmse:6.654835 [3] train-rmse:4.645331 test-rmse:4.743869 [4] train-rmse:3.325456 test-rmse:3.456720 [5] train-rmse:2.423278 test-rmse:2.569874 [6] train-rmse:1.813027 test-rmse:1.966493 [7] train-rmse:1.414601 test-rmse:1.585940 [8] train-rmse:1.163510 test-rmse:1.341057 [9] train-rmse:1.015760 test-rmse:1.191917 [10] train-rmse:0.930486 test-rmse:1.104011 [11] train-rmse:0.881848 test-rmse:1.053831 [12] train-rmse:0.852503 test-rmse:1.074217 [13] train-rmse:0.834754 test-rmse:1.120816 [14] train-rmse:0.819252 test-rmse:1.103515 Stopping. Best iteration: [11] train-rmse:0.881848 test-rmse:1.053831 [1] train-rmse:9.355865 test-rmse:9.009606 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.589386 test-rmse:6.223709 [3] train-rmse:4.666793 test-rmse:4.295465 [4] train-rmse:3.337800 test-rmse:2.974282 [5] train-rmse:2.429685 test-rmse:2.109733 [6] train-rmse:1.818714 test-rmse:1.576740 [7] train-rmse:1.418355 test-rmse:1.267336 [8] train-rmse:1.161812 test-rmse:1.118739 [9] train-rmse:0.997793 test-rmse:1.071095 [10] train-rmse:0.906496 test-rmse:1.064330 [11] train-rmse:0.850724 test-rmse:1.076524 [12] train-rmse:0.821291 test-rmse:1.092378 [13] train-rmse:0.801360 test-rmse:1.108827 Stopping. Best iteration: [10] train-rmse:0.906496 test-rmse:1.064330 [1] train-rmse:9.313774 test-rmse:9.331877 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.563309 test-rmse:6.573938 [3] train-rmse:4.650393 test-rmse:4.621132 [4] train-rmse:3.328604 test-rmse:3.305494 [5] train-rmse:2.425661 test-rmse:2.413088 [6] train-rmse:1.814915 test-rmse:1.853259 [7] train-rmse:1.419793 test-rmse:1.503803 [8] train-rmse:1.172659 test-rmse:1.284221 [9] train-rmse:1.022644 test-rmse:1.185109 [10] train-rmse:0.934843 test-rmse:1.119772 [11] train-rmse:0.888072 test-rmse:1.089436 [12] train-rmse:0.858362 test-rmse:1.083012 [13] train-rmse:0.842531 test-rmse:1.076433 [14] train-rmse:0.832419 test-rmse:1.086406 [15] train-rmse:0.827147 test-rmse:1.085762 [16] train-rmse:0.822292 test-rmse:1.084460 Stopping. Best iteration: [13] train-rmse:0.842531 test-rmse:1.076433 [1] train-rmse:9.306423 test-rmse:9.498313 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.562667 test-rmse:6.811954 [3] train-rmse:4.658224 test-rmse:4.891619 [4] train-rmse:3.342052 test-rmse:3.652696 [5] train-rmse:2.441659 test-rmse:2.771532 [6] train-rmse:1.840506 test-rmse:2.172437 [7] train-rmse:1.446936 test-rmse:1.781696 [8] train-rmse:1.203017 test-rmse:1.493173 [9] train-rmse:1.059839 test-rmse:1.303125 [10] train-rmse:0.973016 test-rmse:1.153319 [11] train-rmse:0.922840 test-rmse:1.059706 [12] train-rmse:0.889719 test-rmse:1.007300 [13] train-rmse:0.871048 test-rmse:0.978629 [14] train-rmse:0.859542 test-rmse:0.952896 [15] train-rmse:0.851813 test-rmse:0.921149 [16] train-rmse:0.845442 test-rmse:0.909978 [17] train-rmse:0.842252 test-rmse:0.904647 [18] train-rmse:0.838153 test-rmse:0.898448 [19] train-rmse:0.837142 test-rmse:0.889976 [20] train-rmse:0.832163 test-rmse:0.874310
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.688399 test-rmse:8.680155 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.107814 test-rmse:6.033188 [3] train-rmse:4.305326 test-rmse:4.203463 [4] train-rmse:3.051046 test-rmse:2.914932 [5] train-rmse:2.183363 test-rmse:2.050512 [6] train-rmse:1.588810 test-rmse:1.512971 [7] train-rmse:1.189600 test-rmse:1.089831 [8] train-rmse:0.923817 test-rmse:0.846376 [9] train-rmse:0.753916 test-rmse:0.706526 [10] train-rmse:0.653023 test-rmse:0.624404 [11] train-rmse:0.594772 test-rmse:0.585188 [12] train-rmse:0.560718 test-rmse:0.561938 [13] train-rmse:0.543520 test-rmse:0.550691 [14] train-rmse:0.532374 test-rmse:0.545028 [15] train-rmse:0.526772 test-rmse:0.541971 [16] train-rmse:0.523087 test-rmse:0.541301 [17] train-rmse:0.518775 test-rmse:0.540691 [18] train-rmse:0.516721 test-rmse:0.537042 [19] train-rmse:0.515183 test-rmse:0.545139 [20] train-rmse:0.514682 test-rmse:0.545645 [1] train-rmse:8.609686 test-rmse:9.115432 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.051174 test-rmse:6.507767 [3] train-rmse:4.264724 test-rmse:4.685622 [4] train-rmse:3.022166 test-rmse:3.416588 [5] train-rmse:2.159828 test-rmse:2.513127 [6] train-rmse:1.569267 test-rmse:1.876565 [7] train-rmse:1.170222 test-rmse:1.462565 [8] train-rmse:0.905819 test-rmse:1.275656 [9] train-rmse:0.736934 test-rmse:1.108353 [10] train-rmse:0.636458 test-rmse:1.029358 [11] train-rmse:0.576192 test-rmse:0.909405 [12] train-rmse:0.542914 test-rmse:0.826708 [13] train-rmse:0.523675 test-rmse:0.829838 [14] train-rmse:0.512547 test-rmse:0.836434 [15] train-rmse:0.506746 test-rmse:0.827045 Stopping. Best iteration: [12] train-rmse:0.542914 test-rmse:0.826708 [1] train-rmse:8.700662 test-rmse:8.578168 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.113994 test-rmse:5.966627 [3] train-rmse:4.307242 test-rmse:4.178606 [4] train-rmse:3.049297 test-rmse:2.945699 [5] train-rmse:2.176590 test-rmse:2.081557 [6] train-rmse:1.576527 test-rmse:1.503069 [7] train-rmse:1.172185 test-rmse:1.146723 [8] train-rmse:0.904225 test-rmse:0.943023 [9] train-rmse:0.735617 test-rmse:0.824187 [10] train-rmse:0.629189 test-rmse:0.744534 [11] train-rmse:0.566774 test-rmse:0.715827 [12] train-rmse:0.531296 test-rmse:0.697464 [13] train-rmse:0.512083 test-rmse:0.690881 [14] train-rmse:0.500679 test-rmse:0.688932 [15] train-rmse:0.494392 test-rmse:0.693134 [16] train-rmse:0.489919 test-rmse:0.694970 [17] train-rmse:0.488018 test-rmse:0.695168 Stopping. Best iteration: [14] train-rmse:0.500679 test-rmse:0.688932 [1] train-rmse:8.757557 test-rmse:8.250955 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.157150 test-rmse:5.661092 [3] train-rmse:4.341194 test-rmse:3.957979 [4] train-rmse:3.077461 test-rmse:2.782839 [5] train-rmse:2.201476 test-rmse:1.978170 [6] train-rmse:1.603837 test-rmse:1.397238 [7] train-rmse:1.194407 test-rmse:0.995174 [8] train-rmse:0.928418 test-rmse:0.750207 [9] train-rmse:0.760468 test-rmse:0.619851 [10] train-rmse:0.658591 test-rmse:0.553802 [11] train-rmse:0.600642 test-rmse:0.523902 [12] train-rmse:0.564737 test-rmse:0.518162 [13] train-rmse:0.544870 test-rmse:0.531997 [14] train-rmse:0.534580 test-rmse:0.549884 [15] train-rmse:0.526906 test-rmse:0.556100 Stopping. Best iteration: [12] train-rmse:0.564737 test-rmse:0.518162 [1] train-rmse:8.664916 test-rmse:8.778420 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.086839 test-rmse:6.231198 [3] train-rmse:4.286693 test-rmse:4.464100 [4] train-rmse:3.034181 test-rmse:3.241929 [5] train-rmse:2.166760 test-rmse:2.367838 [6] train-rmse:1.572694 test-rmse:1.777794 [7] train-rmse:1.171050 test-rmse:1.384651 [8] train-rmse:0.901880 test-rmse:1.128980 [9] train-rmse:0.733444 test-rmse:0.966672 [10] train-rmse:0.632058 test-rmse:0.892128 [11] train-rmse:0.572688 test-rmse:0.852161 [12] train-rmse:0.538299 test-rmse:0.823292 [13] train-rmse:0.520226 test-rmse:0.807063 [14] train-rmse:0.509987 test-rmse:0.800072 [15] train-rmse:0.503747 test-rmse:0.797654 [16] train-rmse:0.500844 test-rmse:0.796888 [17] train-rmse:0.497849 test-rmse:0.796724 [18] train-rmse:0.494351 test-rmse:0.796579 [19] train-rmse:0.490116 test-rmse:0.797492 [20] train-rmse:0.488545 test-rmse:0.798771
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.191752 test-rmse:9.022347 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.462654 test-rmse:6.324937 [3] train-rmse:4.559793 test-rmse:4.488082 [4] train-rmse:3.239145 test-rmse:3.225887 [5] train-rmse:2.328101 test-rmse:2.360096 [6] train-rmse:1.706417 test-rmse:1.793526 [7] train-rmse:1.291706 test-rmse:1.436253 [8] train-rmse:1.025920 test-rmse:1.220558 [9] train-rmse:0.862495 test-rmse:1.074105 [10] train-rmse:0.768017 test-rmse:1.005124 [11] train-rmse:0.715606 test-rmse:0.968217 [12] train-rmse:0.686480 test-rmse:0.953791 [13] train-rmse:0.670951 test-rmse:0.941639 [14] train-rmse:0.659059 test-rmse:0.936490 [15] train-rmse:0.652430 test-rmse:0.932575 [16] train-rmse:0.649407 test-rmse:0.930697 [17] train-rmse:0.646275 test-rmse:0.930612 [18] train-rmse:0.645310 test-rmse:0.929106 [19] train-rmse:0.642627 test-rmse:0.930076 [20] train-rmse:0.638726 test-rmse:0.929579 [1] train-rmse:9.144520 test-rmse:9.287501 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.429130 test-rmse:6.535022 [3] train-rmse:4.537020 test-rmse:4.612913 [4] train-rmse:3.224188 test-rmse:3.297809 [5] train-rmse:2.319442 test-rmse:2.368191 [6] train-rmse:1.704818 test-rmse:1.775405 [7] train-rmse:1.296662 test-rmse:1.363604 [8] train-rmse:1.035958 test-rmse:1.112392 [9] train-rmse:0.877449 test-rmse:0.965193 [10] train-rmse:0.784929 test-rmse:0.889610 [11] train-rmse:0.730773 test-rmse:0.849596 [12] train-rmse:0.701931 test-rmse:0.838960 [13] train-rmse:0.686237 test-rmse:0.833887 [14] train-rmse:0.675761 test-rmse:0.834997 [15] train-rmse:0.670426 test-rmse:0.841563 [16] train-rmse:0.666739 test-rmse:0.849117 Stopping. Best iteration: [13] train-rmse:0.686237 test-rmse:0.833887 [1] train-rmse:9.153772 test-rmse:9.173404 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.435817 test-rmse:6.411588 [3] train-rmse:4.541315 test-rmse:4.511185 [4] train-rmse:3.225657 test-rmse:3.221164 [5] train-rmse:2.319342 test-rmse:2.334455 [6] train-rmse:1.700065 test-rmse:1.717458 [7] train-rmse:1.287691 test-rmse:1.334296 [8] train-rmse:1.023860 test-rmse:1.105660 [9] train-rmse:0.862145 test-rmse:0.976583 [10] train-rmse:0.770260 test-rmse:0.906832 [11] train-rmse:0.718245 test-rmse:0.872306 [12] train-rmse:0.687277 test-rmse:0.848318 [13] train-rmse:0.669527 test-rmse:0.841530 [14] train-rmse:0.659443 test-rmse:0.836041 [15] train-rmse:0.653145 test-rmse:0.830630 [16] train-rmse:0.648007 test-rmse:0.828727 [17] train-rmse:0.644787 test-rmse:0.824171 [18] train-rmse:0.641908 test-rmse:0.823783 [19] train-rmse:0.639047 test-rmse:0.825186 [20] train-rmse:0.636154 test-rmse:0.826109 [1] train-rmse:9.180656 test-rmse:9.116774 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.454295 test-rmse:6.425156 [3] train-rmse:4.553747 test-rmse:4.571362 [4] train-rmse:3.234756 test-rmse:3.259021 [5] train-rmse:2.326379 test-rmse:2.383653 [6] train-rmse:1.706119 test-rmse:1.755711 [7] train-rmse:1.294957 test-rmse:1.369823 [8] train-rmse:1.030159 test-rmse:1.134389 [9] train-rmse:0.868664 test-rmse:0.989395 [10] train-rmse:0.775610 test-rmse:0.918466 [11] train-rmse:0.720952 test-rmse:0.863389 [12] train-rmse:0.691484 test-rmse:0.841419 [13] train-rmse:0.674856 test-rmse:0.832547 [14] train-rmse:0.665361 test-rmse:0.827540 [15] train-rmse:0.660120 test-rmse:0.824473 [16] train-rmse:0.656563 test-rmse:0.820998 [17] train-rmse:0.653762 test-rmse:0.821249 [18] train-rmse:0.650694 test-rmse:0.825186 [19] train-rmse:0.647620 test-rmse:0.820385 [20] train-rmse:0.643023 test-rmse:0.821549 [1] train-rmse:9.158510 test-rmse:9.267916 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.441935 test-rmse:6.586292 [3] train-rmse:4.549883 test-rmse:4.710102 [4] train-rmse:3.235590 test-rmse:3.394203 [5] train-rmse:2.330331 test-rmse:2.520690 [6] train-rmse:1.716194 test-rmse:2.153977 [7] train-rmse:1.311482 test-rmse:1.903843 [8] train-rmse:1.049313 test-rmse:1.616508 [9] train-rmse:0.888993 test-rmse:1.434471 [10] train-rmse:0.796880 test-rmse:1.312197 [11] train-rmse:0.746176 test-rmse:1.228901 [12] train-rmse:0.718002 test-rmse:1.178599 [13] train-rmse:0.702739 test-rmse:1.145342 [14] train-rmse:0.693238 test-rmse:1.123751 [15] train-rmse:0.685394 test-rmse:1.105043 [16] train-rmse:0.680542 test-rmse:1.090681 [17] train-rmse:0.677838 test-rmse:1.084836 [18] train-rmse:0.673977 test-rmse:1.083124 [19] train-rmse:0.670729 test-rmse:1.067827 [20] train-rmse:0.667679 test-rmse:1.067565
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.563392 test-rmse:9.745394 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.780100 test-rmse:6.956004 [3] train-rmse:4.857065 test-rmse:5.045332 [4] train-rmse:3.543758 test-rmse:3.722389 [5] train-rmse:2.660715 test-rmse:2.825827 [6] train-rmse:2.078499 test-rmse:2.175583 [7] train-rmse:1.714831 test-rmse:1.784294 [8] train-rmse:1.493439 test-rmse:1.565866 [9] train-rmse:1.354104 test-rmse:1.392992 [10] train-rmse:1.271567 test-rmse:1.349625 [11] train-rmse:1.216428 test-rmse:1.311383 [12] train-rmse:1.184532 test-rmse:1.298206 [13] train-rmse:1.168332 test-rmse:1.283610 [14] train-rmse:1.152267 test-rmse:1.288226 [15] train-rmse:1.143547 test-rmse:1.275804 [16] train-rmse:1.134514 test-rmse:1.274402 [17] train-rmse:1.118940 test-rmse:1.263849 [18] train-rmse:1.109500 test-rmse:1.281605 [19] train-rmse:1.093315 test-rmse:1.284582 [20] train-rmse:1.083455 test-rmse:1.284100 Stopping. Best iteration: [17] train-rmse:1.118940 test-rmse:1.263849 [1] train-rmse:9.595261 test-rmse:9.505519 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.801487 test-rmse:6.690429 [3] train-rmse:4.870145 test-rmse:4.720175 [4] train-rmse:3.550813 test-rmse:3.381330 [5] train-rmse:2.656242 test-rmse:2.480702 [6] train-rmse:2.072769 test-rmse:1.970050 [7] train-rmse:1.704146 test-rmse:1.631751 [8] train-rmse:1.476155 test-rmse:1.429558 [9] train-rmse:1.348114 test-rmse:1.329899 [10] train-rmse:1.268624 test-rmse:1.310181 [11] train-rmse:1.217940 test-rmse:1.294866 [12] train-rmse:1.186583 test-rmse:1.290854 [13] train-rmse:1.166663 test-rmse:1.305933 [14] train-rmse:1.152506 test-rmse:1.306770 [15] train-rmse:1.133748 test-rmse:1.301688 Stopping. Best iteration: [12] train-rmse:1.186583 test-rmse:1.290854 [1] train-rmse:9.640446 test-rmse:9.172914 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.817282 test-rmse:6.397808 [3] train-rmse:4.864099 test-rmse:4.511844 [4] train-rmse:3.526009 test-rmse:3.271775 [5] train-rmse:2.620144 test-rmse:2.454714 [6] train-rmse:2.022602 test-rmse:1.999959 [7] train-rmse:1.639507 test-rmse:1.786229 [8] train-rmse:1.406856 test-rmse:1.710589 [9] train-rmse:1.270919 test-rmse:1.695777 [10] train-rmse:1.185166 test-rmse:1.720193 [11] train-rmse:1.138147 test-rmse:1.738970 [12] train-rmse:1.110747 test-rmse:1.763409 Stopping. Best iteration: [9] train-rmse:1.270919 test-rmse:1.695777 [1] train-rmse:9.600737 test-rmse:9.471293 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.804638 test-rmse:6.683791 [3] train-rmse:4.871521 test-rmse:4.749756 [4] train-rmse:3.548822 test-rmse:3.423023 [5] train-rmse:2.655225 test-rmse:2.545959 [6] train-rmse:2.066405 test-rmse:1.984502 [7] train-rmse:1.698889 test-rmse:1.635504 [8] train-rmse:1.470364 test-rmse:1.442179 [9] train-rmse:1.335016 test-rmse:1.337769 [10] train-rmse:1.255611 test-rmse:1.292422 [11] train-rmse:1.207602 test-rmse:1.273328 [12] train-rmse:1.181651 test-rmse:1.268303 [13] train-rmse:1.162137 test-rmse:1.264099 [14] train-rmse:1.147711 test-rmse:1.263083 [15] train-rmse:1.127261 test-rmse:1.266702 [16] train-rmse:1.106813 test-rmse:1.269242 [17] train-rmse:1.097341 test-rmse:1.276780 Stopping. Best iteration: [14] train-rmse:1.147711 test-rmse:1.263083 [1] train-rmse:9.504496 test-rmse:10.035182 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.732807 test-rmse:7.293307 [3] train-rmse:4.817598 test-rmse:5.367894 [4] train-rmse:3.504830 test-rmse:4.026366 [5] train-rmse:2.619060 test-rmse:3.129497 [6] train-rmse:2.031211 test-rmse:2.513307 [7] train-rmse:1.659199 test-rmse:2.128701 [8] train-rmse:1.424822 test-rmse:1.882947 [9] train-rmse:1.283344 test-rmse:1.738259 [10] train-rmse:1.200439 test-rmse:1.644387 [11] train-rmse:1.155293 test-rmse:1.580308 [12] train-rmse:1.117409 test-rmse:1.549576 [13] train-rmse:1.101183 test-rmse:1.522058 [14] train-rmse:1.088201 test-rmse:1.504164 [15] train-rmse:1.074251 test-rmse:1.479542 [16] train-rmse:1.062172 test-rmse:1.467319 [17] train-rmse:1.054174 test-rmse:1.457189 [18] train-rmse:1.043018 test-rmse:1.459905 [19] train-rmse:1.031291 test-rmse:1.460594 [20] train-rmse:1.024772 test-rmse:1.462772 Stopping. Best iteration: [17] train-rmse:1.054174 test-rmse:1.457189
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.069714 test-rmse:9.176768 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.376319 test-rmse:6.500133 [3] train-rmse:4.497631 test-rmse:4.634098 [4] train-rmse:3.193176 test-rmse:3.345814 [5] train-rmse:2.293722 test-rmse:2.393848 [6] train-rmse:1.684878 test-rmse:1.803336 [7] train-rmse:1.276627 test-rmse:1.422489 [8] train-rmse:1.010887 test-rmse:1.134158 [9] train-rmse:0.849196 test-rmse:1.006056 [10] train-rmse:0.752351 test-rmse:0.920465 [11] train-rmse:0.699945 test-rmse:0.873726 [12] train-rmse:0.671688 test-rmse:0.857171 [13] train-rmse:0.656445 test-rmse:0.838671 [14] train-rmse:0.647069 test-rmse:0.842814 [15] train-rmse:0.637571 test-rmse:0.835808 [16] train-rmse:0.634645 test-rmse:0.832962 [17] train-rmse:0.633047 test-rmse:0.831711 [18] train-rmse:0.630003 test-rmse:0.832440 [19] train-rmse:0.628843 test-rmse:0.831478 [20] train-rmse:0.627440 test-rmse:0.831354 [1] train-rmse:9.117328 test-rmse:8.878585 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.407958 test-rmse:6.169864 [3] train-rmse:4.518951 test-rmse:4.258363 [4] train-rmse:3.206073 test-rmse:2.966520 [5] train-rmse:2.301102 test-rmse:2.118541 [6] train-rmse:1.686321 test-rmse:1.554798 [7] train-rmse:1.277866 test-rmse:1.236529 [8] train-rmse:1.006382 test-rmse:1.072168 [9] train-rmse:0.839751 test-rmse:0.989377 [10] train-rmse:0.741023 test-rmse:0.959611 [11] train-rmse:0.685690 test-rmse:0.952471 [12] train-rmse:0.655756 test-rmse:0.957774 [13] train-rmse:0.638717 test-rmse:0.957803 [14] train-rmse:0.629052 test-rmse:0.966606 Stopping. Best iteration: [11] train-rmse:0.685690 test-rmse:0.952471 [1] train-rmse:9.118466 test-rmse:8.883828 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.412967 test-rmse:6.190591 [3] train-rmse:4.526733 test-rmse:4.251284 [4] train-rmse:3.217278 test-rmse:2.942931 [5] train-rmse:2.312927 test-rmse:2.058693 [6] train-rmse:1.698356 test-rmse:1.467673 [7] train-rmse:1.286288 test-rmse:1.098494 [8] train-rmse:1.020582 test-rmse:0.892196 [9] train-rmse:0.858603 test-rmse:0.796657 [10] train-rmse:0.761949 test-rmse:0.756485 [11] train-rmse:0.708420 test-rmse:0.750718 [12] train-rmse:0.679029 test-rmse:0.752858 [13] train-rmse:0.655591 test-rmse:0.765849 [14] train-rmse:0.644857 test-rmse:0.776416 Stopping. Best iteration: [11] train-rmse:0.708420 test-rmse:0.750718 [1] train-rmse:9.053860 test-rmse:9.291864 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.371393 test-rmse:6.588000 [3] train-rmse:4.502726 test-rmse:4.728063 [4] train-rmse:3.206058 test-rmse:3.427939 [5] train-rmse:2.313843 test-rmse:2.505042 [6] train-rmse:1.710176 test-rmse:1.888238 [7] train-rmse:1.311737 test-rmse:1.444184 [8] train-rmse:1.058281 test-rmse:1.155123 [9] train-rmse:0.902676 test-rmse:0.952735 [10] train-rmse:0.815713 test-rmse:0.813836 [11] train-rmse:0.767579 test-rmse:0.720073 [12] train-rmse:0.735532 test-rmse:0.665860 [13] train-rmse:0.717613 test-rmse:0.629911 [14] train-rmse:0.709472 test-rmse:0.601795 [15] train-rmse:0.705795 test-rmse:0.578622 [16] train-rmse:0.701166 test-rmse:0.568062 [17] train-rmse:0.697921 test-rmse:0.560575 [18] train-rmse:0.694650 test-rmse:0.555749 [19] train-rmse:0.692907 test-rmse:0.550970 [20] train-rmse:0.692209 test-rmse:0.548297 [1] train-rmse:9.068734 test-rmse:9.190308 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.378061 test-rmse:6.500434 [3] train-rmse:4.504059 test-rmse:4.625340 [4] train-rmse:3.200792 test-rmse:3.317830 [5] train-rmse:2.303331 test-rmse:2.416614 [6] train-rmse:1.696092 test-rmse:1.811304 [7] train-rmse:1.293834 test-rmse:1.399968 [8] train-rmse:1.034570 test-rmse:1.121384 [9] train-rmse:0.877549 test-rmse:0.951949 [10] train-rmse:0.786057 test-rmse:0.865073 [11] train-rmse:0.736101 test-rmse:0.829585 [12] train-rmse:0.699492 test-rmse:0.815459 [13] train-rmse:0.682450 test-rmse:0.802083 [14] train-rmse:0.674858 test-rmse:0.818299 [15] train-rmse:0.668195 test-rmse:0.821290 [16] train-rmse:0.665489 test-rmse:0.836116 Stopping. Best iteration: [13] train-rmse:0.682450 test-rmse:0.802083
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.103442 test-rmse:9.440446 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.405425 test-rmse:6.711218 [3] train-rmse:4.528217 test-rmse:4.800460 [4] train-rmse:3.228092 test-rmse:3.467773 [5] train-rmse:2.336868 test-rmse:2.540701 [6] train-rmse:1.735700 test-rmse:1.875588 [7] train-rmse:1.344362 test-rmse:1.433100 [8] train-rmse:1.099926 test-rmse:1.172009 [9] train-rmse:0.953164 test-rmse:1.021029 [10] train-rmse:0.871124 test-rmse:0.932244 [11] train-rmse:0.826209 test-rmse:0.889599 [12] train-rmse:0.800603 test-rmse:0.865268 [13] train-rmse:0.785704 test-rmse:0.855147 [14] train-rmse:0.777531 test-rmse:0.849851 [15] train-rmse:0.772183 test-rmse:0.843832 [16] train-rmse:0.769371 test-rmse:0.844343 [17] train-rmse:0.767273 test-rmse:0.842136 [18] train-rmse:0.765468 test-rmse:0.840271 [19] train-rmse:0.764508 test-rmse:0.839212 [20] train-rmse:0.763982 test-rmse:0.839014 [1] train-rmse:9.160014 test-rmse:9.066879 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.445015 test-rmse:6.338361 [3] train-rmse:4.555116 test-rmse:4.479981 [4] train-rmse:3.246404 test-rmse:3.195323 [5] train-rmse:2.350145 test-rmse:2.296571 [6] train-rmse:1.743240 test-rmse:1.719294 [7] train-rmse:1.348858 test-rmse:1.336568 [8] train-rmse:1.101044 test-rmse:1.118728 [9] train-rmse:0.955097 test-rmse:0.990800 [10] train-rmse:0.871596 test-rmse:0.920614 [11] train-rmse:0.823433 test-rmse:0.895244 [12] train-rmse:0.799819 test-rmse:0.877095 [13] train-rmse:0.786511 test-rmse:0.861360 [14] train-rmse:0.778374 test-rmse:0.857828 [15] train-rmse:0.774057 test-rmse:0.855775 [16] train-rmse:0.772031 test-rmse:0.853337 [17] train-rmse:0.770188 test-rmse:0.847768 [18] train-rmse:0.769450 test-rmse:0.847126 [19] train-rmse:0.767538 test-rmse:0.848192 [20] train-rmse:0.766768 test-rmse:0.841093 [1] train-rmse:9.224236 test-rmse:8.680011 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.487887 test-rmse:5.907095 [3] train-rmse:4.582695 test-rmse:3.982398 [4] train-rmse:3.261401 test-rmse:2.710070 [5] train-rmse:2.354207 test-rmse:1.851094 [6] train-rmse:1.740419 test-rmse:1.332860 [7] train-rmse:1.335757 test-rmse:1.064103 [8] train-rmse:1.081555 test-rmse:0.955278 [9] train-rmse:0.927564 test-rmse:0.930697 [10] train-rmse:0.840408 test-rmse:0.945276 [11] train-rmse:0.793662 test-rmse:0.968153 [12] train-rmse:0.767342 test-rmse:0.990958 Stopping. Best iteration: [9] train-rmse:0.927564 test-rmse:0.930697 [1] train-rmse:9.168552 test-rmse:9.043181 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.451815 test-rmse:6.317370 [3] train-rmse:4.560193 test-rmse:4.406600 [4] train-rmse:3.250042 test-rmse:3.071922 [5] train-rmse:2.352521 test-rmse:2.174369 [6] train-rmse:1.747246 test-rmse:1.613852 [7] train-rmse:1.351146 test-rmse:1.259002 [8] train-rmse:1.102751 test-rmse:1.038541 [9] train-rmse:0.954673 test-rmse:0.927435 [10] train-rmse:0.871847 test-rmse:0.865285 [11] train-rmse:0.826959 test-rmse:0.843180 [12] train-rmse:0.801470 test-rmse:0.832355 [13] train-rmse:0.788444 test-rmse:0.831472 [14] train-rmse:0.780862 test-rmse:0.830144 [15] train-rmse:0.777425 test-rmse:0.834021 [16] train-rmse:0.774860 test-rmse:0.832896 [17] train-rmse:0.773581 test-rmse:0.834876 Stopping. Best iteration: [14] train-rmse:0.780862 test-rmse:0.830144 [1] train-rmse:9.096748 test-rmse:9.479076 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.402094 test-rmse:6.720445 [3] train-rmse:4.528146 test-rmse:4.805880 [4] train-rmse:3.230842 test-rmse:3.478064 [5] train-rmse:2.339657 test-rmse:2.546777 [6] train-rmse:1.740973 test-rmse:1.914759 [7] train-rmse:1.353456 test-rmse:1.502530 [8] train-rmse:1.112059 test-rmse:1.228800 [9] train-rmse:0.970607 test-rmse:1.058712 [10] train-rmse:0.891539 test-rmse:0.936389 [11] train-rmse:0.849313 test-rmse:0.876336 [12] train-rmse:0.822614 test-rmse:0.820010 [13] train-rmse:0.809062 test-rmse:0.791765 [14] train-rmse:0.801856 test-rmse:0.774863 [15] train-rmse:0.796238 test-rmse:0.760920 [16] train-rmse:0.793618 test-rmse:0.753117 [17] train-rmse:0.790276 test-rmse:0.747800 [18] train-rmse:0.788962 test-rmse:0.746066 [19] train-rmse:0.788470 test-rmse:0.744100 [20] train-rmse:0.787146 test-rmse:0.742847
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.693588 test-rmse:9.046913 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.107000 test-rmse:6.405184 [3] train-rmse:4.302058 test-rmse:4.529536 [4] train-rmse:3.047320 test-rmse:3.219722 [5] train-rmse:2.178156 test-rmse:2.330622 [6] train-rmse:1.583341 test-rmse:1.707188 [7] train-rmse:1.180709 test-rmse:1.277546 [8] train-rmse:0.917474 test-rmse:0.990264 [9] train-rmse:0.748972 test-rmse:0.808056 [10] train-rmse:0.645946 test-rmse:0.705838 [11] train-rmse:0.588825 test-rmse:0.645635 [12] train-rmse:0.556338 test-rmse:0.618674 [13] train-rmse:0.537412 test-rmse:0.625540 [14] train-rmse:0.526473 test-rmse:0.607995 [15] train-rmse:0.517524 test-rmse:0.601278 [16] train-rmse:0.512309 test-rmse:0.613585 [17] train-rmse:0.509701 test-rmse:0.612192 [18] train-rmse:0.507502 test-rmse:0.611567 Stopping. Best iteration: [15] train-rmse:0.517524 test-rmse:0.601278 [1] train-rmse:8.743456 test-rmse:8.812207 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.143353 test-rmse:6.195915 [3] train-rmse:4.329812 test-rmse:4.355691 [4] train-rmse:3.068374 test-rmse:3.062484 [5] train-rmse:2.195959 test-rmse:2.196779 [6] train-rmse:1.597831 test-rmse:1.603471 [7] train-rmse:1.194779 test-rmse:1.200369 [8] train-rmse:0.929608 test-rmse:0.933172 [9] train-rmse:0.764423 test-rmse:0.770942 [10] train-rmse:0.665827 test-rmse:0.670217 [11] train-rmse:0.606871 test-rmse:0.617089 [12] train-rmse:0.571788 test-rmse:0.587325 [13] train-rmse:0.552449 test-rmse:0.570588 [14] train-rmse:0.541485 test-rmse:0.564445 [15] train-rmse:0.533831 test-rmse:0.559609 [16] train-rmse:0.530837 test-rmse:0.557196 [17] train-rmse:0.528909 test-rmse:0.555507 [18] train-rmse:0.526922 test-rmse:0.556388 [19] train-rmse:0.525204 test-rmse:0.558699 [20] train-rmse:0.522876 test-rmse:0.556438 Stopping. Best iteration: [17] train-rmse:0.528909 test-rmse:0.555507 [1] train-rmse:8.842530 test-rmse:8.362955 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.212887 test-rmse:5.837205 [3] train-rmse:4.378079 test-rmse:4.070119 [4] train-rmse:3.102492 test-rmse:2.839036 [5] train-rmse:2.218653 test-rmse:2.021661 [6] train-rmse:1.611419 test-rmse:1.473860 [7] train-rmse:1.203261 test-rmse:1.079364 [8] train-rmse:0.934674 test-rmse:0.823225 [9] train-rmse:0.764657 test-rmse:0.685900 [10] train-rmse:0.660720 test-rmse:0.601680 [11] train-rmse:0.602429 test-rmse:0.553620 [12] train-rmse:0.569491 test-rmse:0.525835 [13] train-rmse:0.550970 test-rmse:0.510122 [14] train-rmse:0.540666 test-rmse:0.503925 [15] train-rmse:0.534497 test-rmse:0.498703 [16] train-rmse:0.529117 test-rmse:0.502594 [17] train-rmse:0.524970 test-rmse:0.504566 [18] train-rmse:0.523369 test-rmse:0.505696 Stopping. Best iteration: [15] train-rmse:0.534497 test-rmse:0.498703 [1] train-rmse:8.770561 test-rmse:8.710547 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.160015 test-rmse:6.141034 [3] train-rmse:4.337746 test-rmse:4.356642 [4] train-rmse:3.068389 test-rmse:3.104195 [5] train-rmse:2.189139 test-rmse:2.237484 [6] train-rmse:1.583704 test-rmse:1.647410 [7] train-rmse:1.174358 test-rmse:1.272548 [8] train-rmse:0.903236 test-rmse:1.024827 [9] train-rmse:0.731628 test-rmse:0.870630 [10] train-rmse:0.626041 test-rmse:0.788578 [11] train-rmse:0.565889 test-rmse:0.746997 [12] train-rmse:0.532413 test-rmse:0.728067 [13] train-rmse:0.514196 test-rmse:0.718716 [14] train-rmse:0.501576 test-rmse:0.710500 [15] train-rmse:0.494880 test-rmse:0.711658 [16] train-rmse:0.490995 test-rmse:0.711432 [17] train-rmse:0.487310 test-rmse:0.722966 Stopping. Best iteration: [14] train-rmse:0.501576 test-rmse:0.710500 [1] train-rmse:8.723936 test-rmse:8.830969 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.125927 test-rmse:6.165722 [3] train-rmse:4.312521 test-rmse:4.349915 [4] train-rmse:3.049617 test-rmse:3.098243 [5] train-rmse:2.173138 test-rmse:2.230675 [6] train-rmse:1.571318 test-rmse:1.644386 [7] train-rmse:1.162255 test-rmse:1.282372 [8] train-rmse:0.889310 test-rmse:1.057627 [9] train-rmse:0.714588 test-rmse:0.911823 [10] train-rmse:0.606908 test-rmse:0.837080 [11] train-rmse:0.544783 test-rmse:0.794975 [12] train-rmse:0.510014 test-rmse:0.773989 [13] train-rmse:0.490459 test-rmse:0.762743 [14] train-rmse:0.480227 test-rmse:0.757475 [15] train-rmse:0.473883 test-rmse:0.753771 [16] train-rmse:0.469978 test-rmse:0.752808 [17] train-rmse:0.467009 test-rmse:0.751810 [18] train-rmse:0.464871 test-rmse:0.762107 [19] train-rmse:0.458940 test-rmse:0.763536 [20] train-rmse:0.453834 test-rmse:0.762878 Stopping. Best iteration: [17] train-rmse:0.467009 test-rmse:0.751810
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.428765 test-rmse:8.391308 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.915066 test-rmse:5.874045 [3] train-rmse:4.159394 test-rmse:4.114336 [4] train-rmse:2.934568 test-rmse:2.923150 [5] train-rmse:2.083916 test-rmse:2.120154 [6] train-rmse:1.496314 test-rmse:1.574440 [7] train-rmse:1.096976 test-rmse:1.202912 [8] train-rmse:0.832607 test-rmse:0.967708 [9] train-rmse:0.661896 test-rmse:0.808089 [10] train-rmse:0.558058 test-rmse:0.715048 [11] train-rmse:0.498678 test-rmse:0.649285 [12] train-rmse:0.466299 test-rmse:0.608436 [13] train-rmse:0.448765 test-rmse:0.585025 [14] train-rmse:0.439245 test-rmse:0.583131 [15] train-rmse:0.434213 test-rmse:0.574947 [16] train-rmse:0.431635 test-rmse:0.573222 [17] train-rmse:0.430213 test-rmse:0.569665 [18] train-rmse:0.429416 test-rmse:0.571454 [19] train-rmse:0.428990 test-rmse:0.570399 [20] train-rmse:0.428596 test-rmse:0.569761 Stopping. Best iteration: [17] train-rmse:0.430213 test-rmse:0.569665 [1] train-rmse:8.426189 test-rmse:8.406964 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.912799 test-rmse:5.858517 [3] train-rmse:4.157116 test-rmse:4.077984 [4] train-rmse:2.932825 test-rmse:2.815647 [5] train-rmse:2.082441 test-rmse:1.961455 [6] train-rmse:1.495380 test-rmse:1.368340 [7] train-rmse:1.095045 test-rmse:1.090383 [8] train-rmse:0.829121 test-rmse:0.827043 [9] train-rmse:0.657511 test-rmse:0.661740 [10] train-rmse:0.552112 test-rmse:0.598997 [11] train-rmse:0.492022 test-rmse:0.542180 [12] train-rmse:0.458798 test-rmse:0.527041 [13] train-rmse:0.441052 test-rmse:0.509151 [14] train-rmse:0.430832 test-rmse:0.500007 [15] train-rmse:0.425712 test-rmse:0.495375 [16] train-rmse:0.422437 test-rmse:0.493120 [17] train-rmse:0.420851 test-rmse:0.492360 [18] train-rmse:0.419577 test-rmse:0.492209 [19] train-rmse:0.418733 test-rmse:0.492407 [20] train-rmse:0.418228 test-rmse:0.493006 [1] train-rmse:8.400713 test-rmse:8.558602 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.894210 test-rmse:6.057575 [3] train-rmse:4.143346 test-rmse:4.319559 [4] train-rmse:2.923088 test-rmse:3.114702 [5] train-rmse:2.074035 test-rmse:2.253753 [6] train-rmse:1.485492 test-rmse:1.673835 [7] train-rmse:1.083306 test-rmse:1.281453 [8] train-rmse:0.814967 test-rmse:1.018210 [9] train-rmse:0.640183 test-rmse:0.870953 [10] train-rmse:0.532241 test-rmse:0.759300 [11] train-rmse:0.468845 test-rmse:0.686398 [12] train-rmse:0.434353 test-rmse:0.641491 [13] train-rmse:0.414383 test-rmse:0.618996 [14] train-rmse:0.403469 test-rmse:0.609458 [15] train-rmse:0.397504 test-rmse:0.601750 [16] train-rmse:0.394164 test-rmse:0.594950 [17] train-rmse:0.392241 test-rmse:0.592459 [18] train-rmse:0.390849 test-rmse:0.590986 [19] train-rmse:0.390257 test-rmse:0.588828 [20] train-rmse:0.389709 test-rmse:0.587411 [1] train-rmse:8.440345 test-rmse:8.315207 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.922210 test-rmse:5.846654 [3] train-rmse:4.163095 test-rmse:4.122849 [4] train-rmse:2.935931 test-rmse:2.886700 [5] train-rmse:2.080860 test-rmse:2.081290 [6] train-rmse:1.490065 test-rmse:1.514267 [7] train-rmse:1.087973 test-rmse:1.148307 [8] train-rmse:0.817509 test-rmse:0.930357 [9] train-rmse:0.641786 test-rmse:0.713593 [10] train-rmse:0.533774 test-rmse:0.659545 [11] train-rmse:0.470216 test-rmse:0.639036 [12] train-rmse:0.434034 test-rmse:0.625304 [13] train-rmse:0.413866 test-rmse:0.645351 [14] train-rmse:0.402915 test-rmse:0.670618 [15] train-rmse:0.397017 test-rmse:0.697143 Stopping. Best iteration: [12] train-rmse:0.434034 test-rmse:0.625304 [1] train-rmse:8.420110 test-rmse:8.447689 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.909743 test-rmse:5.912588 [3] train-rmse:4.156488 test-rmse:4.152623 [4] train-rmse:2.934195 test-rmse:2.902436 [5] train-rmse:2.083920 test-rmse:2.091052 [6] train-rmse:1.497359 test-rmse:1.501792 [7] train-rmse:1.098457 test-rmse:1.127986 [8] train-rmse:0.834448 test-rmse:0.866729 [9] train-rmse:0.666981 test-rmse:0.745932 [10] train-rmse:0.565003 test-rmse:0.656567 [11] train-rmse:0.506768 test-rmse:0.605824 [12] train-rmse:0.474491 test-rmse:0.577663 [13] train-rmse:0.456553 test-rmse:0.558329 [14] train-rmse:0.447470 test-rmse:0.547410 [15] train-rmse:0.441598 test-rmse:0.551575 [16] train-rmse:0.438842 test-rmse:0.545479 [17] train-rmse:0.436861 test-rmse:0.556478 [18] train-rmse:0.435575 test-rmse:0.552566 [19] train-rmse:0.434931 test-rmse:0.549836 Stopping. Best iteration: [16] train-rmse:0.438842 test-rmse:0.545479
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.892025 test-rmse:8.832568 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.253875 test-rmse:6.204197 [3] train-rmse:4.416176 test-rmse:4.367105 [4] train-rmse:3.138502 test-rmse:3.111636 [5] train-rmse:2.258175 test-rmse:2.227856 [6] train-rmse:1.661136 test-rmse:1.628093 [7] train-rmse:1.265752 test-rmse:1.241057 [8] train-rmse:1.013913 test-rmse:1.011655 [9] train-rmse:0.861767 test-rmse:0.879952 [10] train-rmse:0.773110 test-rmse:0.801011 [11] train-rmse:0.725083 test-rmse:0.752121 [12] train-rmse:0.698978 test-rmse:0.728897 [13] train-rmse:0.685526 test-rmse:0.716535 [14] train-rmse:0.678936 test-rmse:0.713030 [15] train-rmse:0.675264 test-rmse:0.713069 [16] train-rmse:0.672765 test-rmse:0.710763 [17] train-rmse:0.671119 test-rmse:0.708236 [18] train-rmse:0.670026 test-rmse:0.711254 [19] train-rmse:0.668069 test-rmse:0.705977 [20] train-rmse:0.666589 test-rmse:0.705278 [1] train-rmse:8.859928 test-rmse:8.962568 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.234403 test-rmse:6.293089 [3] train-rmse:4.403160 test-rmse:4.396224 [4] train-rmse:3.134144 test-rmse:3.074098 [5] train-rmse:2.259118 test-rmse:2.207601 [6] train-rmse:1.665178 test-rmse:1.559299 [7] train-rmse:1.271511 test-rmse:1.134732 [8] train-rmse:1.021415 test-rmse:0.857625 [9] train-rmse:0.872718 test-rmse:0.697746 [10] train-rmse:0.787030 test-rmse:0.618782 [11] train-rmse:0.740843 test-rmse:0.582688 [12] train-rmse:0.712839 test-rmse:0.574999 [13] train-rmse:0.696513 test-rmse:0.572613 [14] train-rmse:0.689643 test-rmse:0.574971 [15] train-rmse:0.685820 test-rmse:0.578471 [16] train-rmse:0.683367 test-rmse:0.580397 Stopping. Best iteration: [13] train-rmse:0.696513 test-rmse:0.572613 [1] train-rmse:8.817436 test-rmse:9.316962 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.194184 test-rmse:6.785861 [3] train-rmse:4.363309 test-rmse:5.045464 [4] train-rmse:3.088067 test-rmse:3.865411 [5] train-rmse:2.205536 test-rmse:3.125746 [6] train-rmse:1.599970 test-rmse:2.563751 [7] train-rmse:1.193130 test-rmse:2.242529 [8] train-rmse:0.925865 test-rmse:2.017588 [9] train-rmse:0.755572 test-rmse:1.878261 [10] train-rmse:0.655244 test-rmse:1.801175 [11] train-rmse:0.596963 test-rmse:1.757816 [12] train-rmse:0.567203 test-rmse:1.719082 [13] train-rmse:0.551180 test-rmse:1.694259 [14] train-rmse:0.538684 test-rmse:1.686608 [15] train-rmse:0.533198 test-rmse:1.676385 [16] train-rmse:0.528142 test-rmse:1.668044 [17] train-rmse:0.525872 test-rmse:1.660345 [18] train-rmse:0.523120 test-rmse:1.654849 [19] train-rmse:0.521903 test-rmse:1.652597 [20] train-rmse:0.519137 test-rmse:1.566213 [1] train-rmse:8.931839 test-rmse:8.655002 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.283246 test-rmse:6.084669 [3] train-rmse:4.437836 test-rmse:4.236735 [4] train-rmse:3.155658 test-rmse:2.994270 [5] train-rmse:2.273363 test-rmse:2.147976 [6] train-rmse:1.676531 test-rmse:1.653940 [7] train-rmse:1.282243 test-rmse:1.311097 [8] train-rmse:1.030823 test-rmse:1.110527 [9] train-rmse:0.879799 test-rmse:0.980363 [10] train-rmse:0.793687 test-rmse:0.880770 [11] train-rmse:0.747198 test-rmse:0.829722 [12] train-rmse:0.720302 test-rmse:0.804202 [13] train-rmse:0.706110 test-rmse:0.789179 [14] train-rmse:0.698761 test-rmse:0.775647 [15] train-rmse:0.694214 test-rmse:0.762038 [16] train-rmse:0.691256 test-rmse:0.729782 [17] train-rmse:0.689934 test-rmse:0.726591 [18] train-rmse:0.688739 test-rmse:0.706610 [19] train-rmse:0.688218 test-rmse:0.706055 [20] train-rmse:0.687719 test-rmse:0.703007 [1] train-rmse:8.906107 test-rmse:8.766071 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.262795 test-rmse:6.180082 [3] train-rmse:4.420306 test-rmse:4.371736 [4] train-rmse:3.140907 test-rmse:3.123147 [5] train-rmse:2.260619 test-rmse:2.267217 [6] train-rmse:1.661258 test-rmse:1.693467 [7] train-rmse:1.267005 test-rmse:1.286994 [8] train-rmse:1.013863 test-rmse:1.036385 [9] train-rmse:0.858518 test-rmse:0.880098 [10] train-rmse:0.770331 test-rmse:0.801058 [11] train-rmse:0.722747 test-rmse:0.758319 [12] train-rmse:0.696519 test-rmse:0.742073 [13] train-rmse:0.682410 test-rmse:0.728646 [14] train-rmse:0.674716 test-rmse:0.724577 [15] train-rmse:0.669255 test-rmse:0.724556 [16] train-rmse:0.666714 test-rmse:0.721589 [17] train-rmse:0.664532 test-rmse:0.724363 [18] train-rmse:0.663547 test-rmse:0.724630 [19] train-rmse:0.663083 test-rmse:0.724686 Stopping. Best iteration: [16] train-rmse:0.666714 test-rmse:0.721589
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.452502 test-rmse:9.642451 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.685195 test-rmse:6.733642 [3] train-rmse:4.765635 test-rmse:4.798407 [4] train-rmse:3.443268 test-rmse:3.360954 [5] train-rmse:2.544030 test-rmse:2.461216 [6] train-rmse:1.945822 test-rmse:1.825011 [7] train-rmse:1.555000 test-rmse:1.453659 [8] train-rmse:1.314020 test-rmse:1.233649 [9] train-rmse:1.171538 test-rmse:1.109235 [10] train-rmse:1.083860 test-rmse:1.040851 [11] train-rmse:1.034104 test-rmse:1.006096 [12] train-rmse:0.997157 test-rmse:0.997726 [13] train-rmse:0.972340 test-rmse:0.992322 [14] train-rmse:0.958659 test-rmse:0.994203 [15] train-rmse:0.949906 test-rmse:0.994200 [16] train-rmse:0.942320 test-rmse:0.999055 Stopping. Best iteration: [13] train-rmse:0.972340 test-rmse:0.992322 [1] train-rmse:9.431807 test-rmse:9.744325 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.661616 test-rmse:6.899324 [3] train-rmse:4.735205 test-rmse:5.043730 [4] train-rmse:3.406299 test-rmse:3.696000 [5] train-rmse:2.501240 test-rmse:2.814101 [6] train-rmse:1.891946 test-rmse:2.231111 [7] train-rmse:1.491235 test-rmse:1.856697 [8] train-rmse:1.239599 test-rmse:1.628386 [9] train-rmse:1.088686 test-rmse:1.487980 [10] train-rmse:1.000789 test-rmse:1.413225 [11] train-rmse:0.947451 test-rmse:1.367817 [12] train-rmse:0.918055 test-rmse:1.350506 [13] train-rmse:0.898136 test-rmse:1.337612 [14] train-rmse:0.884646 test-rmse:1.328003 [15] train-rmse:0.873003 test-rmse:1.325452 [16] train-rmse:0.867923 test-rmse:1.328080 [17] train-rmse:0.863360 test-rmse:1.332772 [18] train-rmse:0.854097 test-rmse:1.337520 Stopping. Best iteration: [15] train-rmse:0.873003 test-rmse:1.325452 [1] train-rmse:9.413054 test-rmse:9.892609 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.646635 test-rmse:7.122437 [3] train-rmse:4.724082 test-rmse:5.237950 [4] train-rmse:3.396812 test-rmse:3.878758 [5] train-rmse:2.485992 test-rmse:2.997833 [6] train-rmse:1.871376 test-rmse:2.414646 [7] train-rmse:1.472166 test-rmse:2.023069 [8] train-rmse:1.212013 test-rmse:1.802773 [9] train-rmse:1.055796 test-rmse:1.651017 [10] train-rmse:0.958232 test-rmse:1.565198 [11] train-rmse:0.903595 test-rmse:1.512697 [12] train-rmse:0.871785 test-rmse:1.486446 [13] train-rmse:0.854470 test-rmse:1.465239 [14] train-rmse:0.837651 test-rmse:1.467007 [15] train-rmse:0.825568 test-rmse:1.467049 [16] train-rmse:0.819721 test-rmse:1.468490 Stopping. Best iteration: [13] train-rmse:0.854470 test-rmse:1.465239 [1] train-rmse:9.532273 test-rmse:9.195878 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.739022 test-rmse:6.478094 [3] train-rmse:4.799496 test-rmse:4.515652 [4] train-rmse:3.460892 test-rmse:3.227286 [5] train-rmse:2.549972 test-rmse:2.358527 [6] train-rmse:1.936807 test-rmse:1.800220 [7] train-rmse:1.542910 test-rmse:1.455549 [8] train-rmse:1.296019 test-rmse:1.238111 [9] train-rmse:1.147287 test-rmse:1.103079 [10] train-rmse:1.062140 test-rmse:1.043861 [11] train-rmse:1.007511 test-rmse:1.012812 [12] train-rmse:0.968397 test-rmse:0.996789 [13] train-rmse:0.945362 test-rmse:0.992567 [14] train-rmse:0.931794 test-rmse:0.989809 [15] train-rmse:0.921453 test-rmse:0.987479 [16] train-rmse:0.915722 test-rmse:0.994906 [17] train-rmse:0.902267 test-rmse:0.997811 [18] train-rmse:0.897239 test-rmse:0.999387 Stopping. Best iteration: [15] train-rmse:0.921453 test-rmse:0.987479 [1] train-rmse:9.566864 test-rmse:8.956793 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.761539 test-rmse:6.268634 [3] train-rmse:4.811765 test-rmse:4.359044 [4] train-rmse:3.467537 test-rmse:3.024432 [5] train-rmse:2.554520 test-rmse:2.154403 [6] train-rmse:1.944125 test-rmse:1.607038 [7] train-rmse:1.549371 test-rmse:1.298705 [8] train-rmse:1.303963 test-rmse:1.137336 [9] train-rmse:1.156145 test-rmse:1.042976 [10] train-rmse:1.065626 test-rmse:0.995586 [11] train-rmse:1.017580 test-rmse:0.985768 [12] train-rmse:0.981443 test-rmse:0.994753 [13] train-rmse:0.960499 test-rmse:0.997249 [14] train-rmse:0.946733 test-rmse:1.003005 Stopping. Best iteration: [11] train-rmse:1.017580 test-rmse:0.985768
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.862701 test-rmse:9.005774 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.220913 test-rmse:6.323318 [3] train-rmse:4.377735 test-rmse:4.453634 [4] train-rmse:3.096045 test-rmse:3.148616 [5] train-rmse:2.209429 test-rmse:2.236210 [6] train-rmse:1.603432 test-rmse:1.615925 [7] train-rmse:1.198335 test-rmse:1.193634 [8] train-rmse:0.936767 test-rmse:0.916316 [9] train-rmse:0.776365 test-rmse:0.733614 [10] train-rmse:0.682530 test-rmse:0.632489 [11] train-rmse:0.631019 test-rmse:0.571557 [12] train-rmse:0.603129 test-rmse:0.545576 [13] train-rmse:0.588800 test-rmse:0.531962 [14] train-rmse:0.581174 test-rmse:0.527433 [15] train-rmse:0.577092 test-rmse:0.524981 [16] train-rmse:0.574999 test-rmse:0.522185 [17] train-rmse:0.573345 test-rmse:0.519564 [18] train-rmse:0.572550 test-rmse:0.521602 [19] train-rmse:0.571922 test-rmse:0.522532 [20] train-rmse:0.571674 test-rmse:0.522265 Stopping. Best iteration: [17] train-rmse:0.573345 test-rmse:0.519564 [1] train-rmse:8.829133 test-rmse:9.146082 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.198418 test-rmse:6.430224 [3] train-rmse:4.363281 test-rmse:4.540604 [4] train-rmse:3.086791 test-rmse:3.215268 [5] train-rmse:2.204443 test-rmse:2.289753 [6] train-rmse:1.602121 test-rmse:1.650367 [7] train-rmse:1.200063 test-rmse:1.212943 [8] train-rmse:0.941239 test-rmse:0.917623 [9] train-rmse:0.782909 test-rmse:0.729564 [10] train-rmse:0.691624 test-rmse:0.612428 [11] train-rmse:0.640790 test-rmse:0.544929 [12] train-rmse:0.613175 test-rmse:0.506634 [13] train-rmse:0.599032 test-rmse:0.483801 [14] train-rmse:0.591934 test-rmse:0.473084 [15] train-rmse:0.586701 test-rmse:0.465547 [16] train-rmse:0.584189 test-rmse:0.461563 [17] train-rmse:0.582687 test-rmse:0.462164 [18] train-rmse:0.581889 test-rmse:0.460413 [19] train-rmse:0.581378 test-rmse:0.462454 [20] train-rmse:0.580842 test-rmse:0.462948 [1] train-rmse:8.875980 test-rmse:8.990371 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.229320 test-rmse:6.379723 [3] train-rmse:4.382633 test-rmse:4.556653 [4] train-rmse:3.098224 test-rmse:3.262463 [5] train-rmse:2.209828 test-rmse:2.365464 [6] train-rmse:1.602630 test-rmse:1.749725 [7] train-rmse:1.196097 test-rmse:1.320192 [8] train-rmse:0.933420 test-rmse:1.030299 [9] train-rmse:0.771977 test-rmse:0.853604 [10] train-rmse:0.677965 test-rmse:0.743883 [11] train-rmse:0.626182 test-rmse:0.677405 [12] train-rmse:0.598762 test-rmse:0.641716 [13] train-rmse:0.583983 test-rmse:0.609458 [14] train-rmse:0.576655 test-rmse:0.595009 [15] train-rmse:0.572653 test-rmse:0.588529 [16] train-rmse:0.570277 test-rmse:0.584529 [17] train-rmse:0.568584 test-rmse:0.581166 [18] train-rmse:0.567575 test-rmse:0.577919 [19] train-rmse:0.567201 test-rmse:0.576218 [20] train-rmse:0.566697 test-rmse:0.574319 [1] train-rmse:8.942529 test-rmse:8.636760 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.275066 test-rmse:6.029676 [3] train-rmse:4.413392 test-rmse:4.209131 [4] train-rmse:3.116383 test-rmse:2.967446 [5] train-rmse:2.217777 test-rmse:2.123125 [6] train-rmse:1.601046 test-rmse:1.541932 [7] train-rmse:1.185831 test-rmse:1.157134 [8] train-rmse:0.914935 test-rmse:0.928149 [9] train-rmse:0.746071 test-rmse:0.792138 [10] train-rmse:0.646407 test-rmse:0.717927 [11] train-rmse:0.590191 test-rmse:0.683656 [12] train-rmse:0.559097 test-rmse:0.666148 [13] train-rmse:0.543127 test-rmse:0.659812 [14] train-rmse:0.532043 test-rmse:0.740491 [15] train-rmse:0.527022 test-rmse:0.750575 [16] train-rmse:0.524574 test-rmse:0.747581 Stopping. Best iteration: [13] train-rmse:0.543127 test-rmse:0.659812 [1] train-rmse:8.936684 test-rmse:8.651659 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.270218 test-rmse:6.030818 [3] train-rmse:4.408925 test-rmse:4.203529 [4] train-rmse:3.112111 test-rmse:2.959849 [5] train-rmse:2.212896 test-rmse:2.110324 [6] train-rmse:1.595451 test-rmse:1.543473 [7] train-rmse:1.179066 test-rmse:1.178747 [8] train-rmse:0.905888 test-rmse:0.950621 [9] train-rmse:0.734403 test-rmse:0.823927 [10] train-rmse:0.632368 test-rmse:0.757649 [11] train-rmse:0.574860 test-rmse:0.726408 [12] train-rmse:0.543827 test-rmse:0.713247 [13] train-rmse:0.527704 test-rmse:0.706223 [14] train-rmse:0.519169 test-rmse:0.703529 [15] train-rmse:0.514640 test-rmse:0.702720 [16] train-rmse:0.512143 test-rmse:0.702413 [17] train-rmse:0.510477 test-rmse:0.702688 [18] train-rmse:0.509358 test-rmse:0.703163 [19] train-rmse:0.508727 test-rmse:0.703280 Stopping. Best iteration: [16] train-rmse:0.512143 test-rmse:0.702413
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.969872 test-rmse:9.035048 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.298522 test-rmse:6.315012 [3] train-rmse:4.435327 test-rmse:4.407293 [4] train-rmse:3.140857 test-rmse:3.089796 [5] train-rmse:2.248063 test-rmse:2.204641 [6] train-rmse:1.638961 test-rmse:1.608183 [7] train-rmse:1.233365 test-rmse:1.239013 [8] train-rmse:0.974806 test-rmse:1.024157 [9] train-rmse:0.816359 test-rmse:0.910068 [10] train-rmse:0.724347 test-rmse:0.860773 [11] train-rmse:0.675180 test-rmse:0.840778 [12] train-rmse:0.648090 test-rmse:0.835742 [13] train-rmse:0.632959 test-rmse:0.835676 [14] train-rmse:0.625821 test-rmse:0.840738 [15] train-rmse:0.622249 test-rmse:0.844628 [16] train-rmse:0.619304 test-rmse:0.847118 Stopping. Best iteration: [13] train-rmse:0.632959 test-rmse:0.835676 [1] train-rmse:8.960952 test-rmse:9.172461 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.297010 test-rmse:6.454947 [3] train-rmse:4.440972 test-rmse:4.554924 [4] train-rmse:3.154120 test-rmse:3.231386 [5] train-rmse:2.270589 test-rmse:2.317710 [6] train-rmse:1.672018 test-rmse:1.688968 [7] train-rmse:1.277071 test-rmse:1.265620 [8] train-rmse:1.027181 test-rmse:0.981108 [9] train-rmse:0.878306 test-rmse:0.799774 [10] train-rmse:0.793086 test-rmse:0.677266 [11] train-rmse:0.747685 test-rmse:0.614541 [12] train-rmse:0.723025 test-rmse:0.573659 [13] train-rmse:0.711141 test-rmse:0.548428 [14] train-rmse:0.703797 test-rmse:0.530851 [15] train-rmse:0.700758 test-rmse:0.523337 [16] train-rmse:0.699159 test-rmse:0.517473 [17] train-rmse:0.698177 test-rmse:0.515549 [18] train-rmse:0.697248 test-rmse:0.512771 [19] train-rmse:0.696459 test-rmse:0.510436 [20] train-rmse:0.695933 test-rmse:0.509217 [1] train-rmse:8.981951 test-rmse:9.031130 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.309666 test-rmse:6.354066 [3] train-rmse:4.447022 test-rmse:4.483027 [4] train-rmse:3.154247 test-rmse:3.183718 [5] train-rmse:2.264572 test-rmse:2.289404 [6] train-rmse:1.661701 test-rmse:1.683871 [7] train-rmse:1.260942 test-rmse:1.284284 [8] train-rmse:1.006320 test-rmse:1.021492 [9] train-rmse:0.852364 test-rmse:0.857634 [10] train-rmse:0.763903 test-rmse:0.766041 [11] train-rmse:0.716488 test-rmse:0.718737 [12] train-rmse:0.691529 test-rmse:0.694032 [13] train-rmse:0.677461 test-rmse:0.683597 [14] train-rmse:0.669641 test-rmse:0.679441 [15] train-rmse:0.666104 test-rmse:0.677250 [16] train-rmse:0.664295 test-rmse:0.674416 [17] train-rmse:0.662425 test-rmse:0.676188 [18] train-rmse:0.661893 test-rmse:0.676316 [19] train-rmse:0.661000 test-rmse:0.678595 Stopping. Best iteration: [16] train-rmse:0.664295 test-rmse:0.674416 [1] train-rmse:9.021715 test-rmse:8.851350 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.336138 test-rmse:6.221504 [3] train-rmse:4.463458 test-rmse:4.381516 [4] train-rmse:3.162833 test-rmse:3.107472 [5] train-rmse:2.264987 test-rmse:2.224344 [6] train-rmse:1.652972 test-rmse:1.630447 [7] train-rmse:1.246201 test-rmse:1.256254 [8] train-rmse:0.985464 test-rmse:1.024567 [9] train-rmse:0.826070 test-rmse:0.904439 [10] train-rmse:0.733773 test-rmse:0.835885 [11] train-rmse:0.683645 test-rmse:0.799277 [12] train-rmse:0.656425 test-rmse:0.784083 [13] train-rmse:0.642047 test-rmse:0.777902 [14] train-rmse:0.634664 test-rmse:0.775785 [15] train-rmse:0.631007 test-rmse:0.773567 [16] train-rmse:0.628747 test-rmse:0.773723 [17] train-rmse:0.627722 test-rmse:0.773191 [18] train-rmse:0.627119 test-rmse:0.773241 [19] train-rmse:0.626795 test-rmse:0.772918 [20] train-rmse:0.626151 test-rmse:0.773598 [1] train-rmse:9.012696 test-rmse:8.906447 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.331256 test-rmse:6.265323 [3] train-rmse:4.462221 test-rmse:4.419940 [4] train-rmse:3.165329 test-rmse:3.126775 [5] train-rmse:2.273229 test-rmse:2.240337 [6] train-rmse:1.669308 test-rmse:1.649943 [7] train-rmse:1.269594 test-rmse:1.242575 [8] train-rmse:1.017064 test-rmse:0.991395 [9] train-rmse:0.865435 test-rmse:0.847606 [10] train-rmse:0.779151 test-rmse:0.764855 [11] train-rmse:0.730069 test-rmse:0.712346 [12] train-rmse:0.704599 test-rmse:0.685934 [13] train-rmse:0.690811 test-rmse:0.669802 [14] train-rmse:0.683848 test-rmse:0.662900 [15] train-rmse:0.679864 test-rmse:0.658532 [16] train-rmse:0.678080 test-rmse:0.656718 [17] train-rmse:0.677022 test-rmse:0.654538 [18] train-rmse:0.676478 test-rmse:0.653458 [19] train-rmse:0.676077 test-rmse:0.653101 [20] train-rmse:0.675795 test-rmse:0.653458
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.578445 test-rmse:8.302501 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.057782 test-rmse:5.793966 [3] train-rmse:4.296401 test-rmse:4.050825 [4] train-rmse:3.057118 test-rmse:2.854931 [5] train-rmse:2.192064 test-rmse:2.023654 [6] train-rmse:1.594539 test-rmse:1.455028 [7] train-rmse:1.185090 test-rmse:1.088486 [8] train-rmse:0.910416 test-rmse:0.859422 [9] train-rmse:0.725968 test-rmse:0.722941 [10] train-rmse:0.603872 test-rmse:0.652238 [11] train-rmse:0.529974 test-rmse:0.637444 [12] train-rmse:0.471805 test-rmse:0.624844 [13] train-rmse:0.433467 test-rmse:0.619281 [14] train-rmse:0.406507 test-rmse:0.607405 [15] train-rmse:0.389322 test-rmse:0.606217 [16] train-rmse:0.379266 test-rmse:0.611445 [17] train-rmse:0.373309 test-rmse:0.624866 [18] train-rmse:0.369351 test-rmse:0.628897 Stopping. Best iteration: [15] train-rmse:0.389322 test-rmse:0.606217 [1] train-rmse:8.655230 test-rmse:7.817839 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.116262 test-rmse:5.265132 [3] train-rmse:4.337394 test-rmse:3.721160 [4] train-rmse:3.090691 test-rmse:2.628811 [5] train-rmse:2.222716 test-rmse:1.857095 [6] train-rmse:1.626850 test-rmse:1.232189 [7] train-rmse:1.222087 test-rmse:0.881256 [8] train-rmse:0.949971 test-rmse:0.670038 [9] train-rmse:0.771874 test-rmse:0.543624 [10] train-rmse:0.663896 test-rmse:0.429528 [11] train-rmse:0.579249 test-rmse:0.424505 [12] train-rmse:0.520653 test-rmse:0.439845 [13] train-rmse:0.489414 test-rmse:0.444767 [14] train-rmse:0.471488 test-rmse:0.414689 [15] train-rmse:0.453963 test-rmse:0.424464 [16] train-rmse:0.445416 test-rmse:0.427689 [17] train-rmse:0.441183 test-rmse:0.435990 Stopping. Best iteration: [14] train-rmse:0.471488 test-rmse:0.414689 [1] train-rmse:8.485212 test-rmse:8.891543 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.000490 test-rmse:6.390955 [3] train-rmse:4.256322 test-rmse:4.422209 [4] train-rmse:3.031806 test-rmse:3.035791 [5] train-rmse:2.177100 test-rmse:2.064057 [6] train-rmse:1.587064 test-rmse:1.390514 [7] train-rmse:1.187767 test-rmse:0.936320 [8] train-rmse:0.926309 test-rmse:0.650777 [9] train-rmse:0.762118 test-rmse:0.502267 [10] train-rmse:0.638438 test-rmse:0.436744 [11] train-rmse:0.552036 test-rmse:0.392647 [12] train-rmse:0.495043 test-rmse:0.368198 [13] train-rmse:0.457438 test-rmse:0.360724 [14] train-rmse:0.433926 test-rmse:0.366935 [15] train-rmse:0.417676 test-rmse:0.374061 [16] train-rmse:0.407147 test-rmse:0.381172 Stopping. Best iteration: [13] train-rmse:0.457438 test-rmse:0.360724 [1] train-rmse:8.513542 test-rmse:8.707479 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.014591 test-rmse:6.212036 [3] train-rmse:4.260466 test-rmse:4.559160 [4] train-rmse:3.028293 test-rmse:3.402098 [5] train-rmse:2.167038 test-rmse:2.590053 [6] train-rmse:1.570585 test-rmse:2.022575 [7] train-rmse:1.164410 test-rmse:1.629125 [8] train-rmse:0.892219 test-rmse:1.341207 [9] train-rmse:0.713109 test-rmse:1.150766 [10] train-rmse:0.600845 test-rmse:1.026622 [11] train-rmse:0.525558 test-rmse:0.960915 [12] train-rmse:0.476484 test-rmse:0.918432 [13] train-rmse:0.448012 test-rmse:0.885938 [14] train-rmse:0.427800 test-rmse:0.870032 [15] train-rmse:0.416532 test-rmse:0.847365 [16] train-rmse:0.408150 test-rmse:0.839793 [17] train-rmse:0.404584 test-rmse:0.827364 [18] train-rmse:0.402372 test-rmse:0.816190 [19] train-rmse:0.400819 test-rmse:0.805229 [20] train-rmse:0.399931 test-rmse:0.798341 [1] train-rmse:8.473876 test-rmse:8.944304 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.987852 test-rmse:6.454243 [3] train-rmse:4.244336 test-rmse:4.565351 [4] train-rmse:3.019459 test-rmse:3.241281 [5] train-rmse:2.163291 test-rmse:2.320282 [6] train-rmse:1.570612 test-rmse:1.689132 [7] train-rmse:1.164390 test-rmse:1.249411 [8] train-rmse:0.889521 test-rmse:0.954917 [9] train-rmse:0.710882 test-rmse:0.813351 [10] train-rmse:0.577675 test-rmse:0.710474 [11] train-rmse:0.488187 test-rmse:0.669665 [12] train-rmse:0.427208 test-rmse:0.647588 [13] train-rmse:0.388365 test-rmse:0.651034 [14] train-rmse:0.365138 test-rmse:0.657087 [15] train-rmse:0.343526 test-rmse:0.663497 Stopping. Best iteration: [12] train-rmse:0.427208 test-rmse:0.647588
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.334099 test-rmse:8.025390 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.903937 test-rmse:5.596196 [3] train-rmse:4.188772 test-rmse:3.882742 [4] train-rmse:2.980827 test-rmse:2.677925 [5] train-rmse:2.133663 test-rmse:1.836644 [6] train-rmse:1.544287 test-rmse:1.258502 [7] train-rmse:1.137393 test-rmse:0.857763 [8] train-rmse:0.861601 test-rmse:0.618884 [9] train-rmse:0.680689 test-rmse:0.510438 [10] train-rmse:0.553775 test-rmse:0.424104 [11] train-rmse:0.472275 test-rmse:0.407594 [12] train-rmse:0.422231 test-rmse:0.424849 [13] train-rmse:0.392743 test-rmse:0.451870 [14] train-rmse:0.375912 test-rmse:0.478173 Stopping. Best iteration: [11] train-rmse:0.472275 test-rmse:0.407594 [1] train-rmse:8.313069 test-rmse:8.162770 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.891214 test-rmse:5.736729 [3] train-rmse:4.182800 test-rmse:4.022487 [4] train-rmse:2.980839 test-rmse:2.812502 [5] train-rmse:2.139541 test-rmse:1.960360 [6] train-rmse:1.556428 test-rmse:1.363098 [7] train-rmse:1.157860 test-rmse:0.930003 [8] train-rmse:0.889499 test-rmse:0.644719 [9] train-rmse:0.702859 test-rmse:0.489168 [10] train-rmse:0.581288 test-rmse:0.392856 [11] train-rmse:0.505813 test-rmse:0.340920 [12] train-rmse:0.459460 test-rmse:0.315641 [13] train-rmse:0.432253 test-rmse:0.305779 [14] train-rmse:0.416704 test-rmse:0.303349 [15] train-rmse:0.407904 test-rmse:0.303811 [16] train-rmse:0.402902 test-rmse:0.305086 [17] train-rmse:0.400064 test-rmse:0.306504 Stopping. Best iteration: [14] train-rmse:0.416704 test-rmse:0.303349 [1] train-rmse:8.294709 test-rmse:8.273802 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.877749 test-rmse:5.854425 [3] train-rmse:4.172603 test-rmse:4.145894 [4] train-rmse:2.972685 test-rmse:2.941272 [5] train-rmse:2.132470 test-rmse:2.094612 [6] train-rmse:1.549658 test-rmse:1.503204 [7] train-rmse:1.152305 test-rmse:1.094929 [8] train-rmse:0.875753 test-rmse:0.841672 [9] train-rmse:0.687748 test-rmse:0.675245 [10] train-rmse:0.564965 test-rmse:0.566438 [11] train-rmse:0.487934 test-rmse:0.499860 [12] train-rmse:0.440964 test-rmse:0.459017 [13] train-rmse:0.413181 test-rmse:0.434891 [14] train-rmse:0.397315 test-rmse:0.420833 [15] train-rmse:0.388443 test-rmse:0.412328 [16] train-rmse:0.383526 test-rmse:0.407244 [17] train-rmse:0.380816 test-rmse:0.404037 [18] train-rmse:0.379326 test-rmse:0.402035 [19] train-rmse:0.378505 test-rmse:0.400709 [20] train-rmse:0.378053 test-rmse:0.399832 [1] train-rmse:8.205634 test-rmse:8.792711 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.813099 test-rmse:6.395546 [3] train-rmse:4.124559 test-rmse:4.700422 [4] train-rmse:2.935461 test-rmse:3.501976 [5] train-rmse:2.101643 test-rmse:2.654949 [6] train-rmse:1.521722 test-rmse:2.056581 [7] train-rmse:1.124447 test-rmse:1.634149 [8] train-rmse:0.859326 test-rmse:1.336153 [9] train-rmse:0.675956 test-rmse:1.091172 [10] train-rmse:0.554694 test-rmse:0.916987 [11] train-rmse:0.477760 test-rmse:0.792150 [12] train-rmse:0.431229 test-rmse:0.704333 [13] train-rmse:0.402341 test-rmse:0.640256 [14] train-rmse:0.384316 test-rmse:0.597012 [15] train-rmse:0.373151 test-rmse:0.568312 [16] train-rmse:0.366273 test-rmse:0.550028 [17] train-rmse:0.362035 test-rmse:0.538871 [18] train-rmse:0.359418 test-rmse:0.532128 [19] train-rmse:0.357797 test-rmse:0.528319 [20] train-rmse:0.356789 test-rmse:0.526382 [1] train-rmse:8.308342 test-rmse:8.184117 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.885253 test-rmse:5.764773 [3] train-rmse:4.174901 test-rmse:4.059872 [4] train-rmse:2.970098 test-rmse:2.863044 [5] train-rmse:2.124796 test-rmse:2.029435 [6] train-rmse:1.536268 test-rmse:1.457893 [7] train-rmse:1.132324 test-rmse:1.077827 [8] train-rmse:0.860757 test-rmse:0.835838 [9] train-rmse:0.679659 test-rmse:0.659356 [10] train-rmse:0.562783 test-rmse:0.590300 [11] train-rmse:0.490861 test-rmse:0.586972 [12] train-rmse:0.448649 test-rmse:0.611937 [13] train-rmse:0.424548 test-rmse:0.652259 [14] train-rmse:0.410751 test-rmse:0.692964 Stopping. Best iteration: [11] train-rmse:0.490861 test-rmse:0.586972
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.451266 test-rmse:8.477498 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.937681 test-rmse:5.964173 [3] train-rmse:4.181331 test-rmse:4.216638 [4] train-rmse:2.958865 test-rmse:3.011042 [5] train-rmse:2.113429 test-rmse:2.173071 [6] train-rmse:1.532697 test-rmse:1.600461 [7] train-rmse:1.141105 test-rmse:1.258772 [8] train-rmse:0.883017 test-rmse:1.035804 [9] train-rmse:0.719268 test-rmse:0.924756 [10] train-rmse:0.618770 test-rmse:0.862429 [11] train-rmse:0.561812 test-rmse:0.830191 [12] train-rmse:0.527488 test-rmse:0.815540 [13] train-rmse:0.508346 test-rmse:0.807689 [14] train-rmse:0.498548 test-rmse:0.806715 [15] train-rmse:0.493109 test-rmse:0.807226 [16] train-rmse:0.490182 test-rmse:0.808725 [17] train-rmse:0.488645 test-rmse:0.810128 Stopping. Best iteration: [14] train-rmse:0.498548 test-rmse:0.806715 [1] train-rmse:8.469831 test-rmse:8.363748 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.950912 test-rmse:5.844436 [3] train-rmse:4.192892 test-rmse:4.086032 [4] train-rmse:2.969622 test-rmse:2.865237 [5] train-rmse:2.125017 test-rmse:2.019831 [6] train-rmse:1.548100 test-rmse:1.545065 [7] train-rmse:1.161451 test-rmse:1.155098 [8] train-rmse:0.908705 test-rmse:0.982487 [9] train-rmse:0.749442 test-rmse:0.872167 [10] train-rmse:0.654746 test-rmse:0.804906 [11] train-rmse:0.598993 test-rmse:0.756558 [12] train-rmse:0.569124 test-rmse:0.720005 [13] train-rmse:0.551903 test-rmse:0.694147 [14] train-rmse:0.542561 test-rmse:0.679711 [15] train-rmse:0.537559 test-rmse:0.670347 [16] train-rmse:0.534763 test-rmse:0.659302 [17] train-rmse:0.533237 test-rmse:0.654571 [18] train-rmse:0.532347 test-rmse:0.655568 [19] train-rmse:0.531844 test-rmse:0.656503 [20] train-rmse:0.531509 test-rmse:0.653030 [1] train-rmse:8.439210 test-rmse:8.550293 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.928944 test-rmse:6.040700 [3] train-rmse:4.176772 test-rmse:4.289297 [4] train-rmse:2.955913 test-rmse:3.066990 [5] train-rmse:2.110766 test-rmse:2.202621 [6] train-rmse:1.532572 test-rmse:1.616989 [7] train-rmse:1.142028 test-rmse:1.230694 [8] train-rmse:0.887889 test-rmse:0.982648 [9] train-rmse:0.727042 test-rmse:0.838598 [10] train-rmse:0.630578 test-rmse:0.760078 [11] train-rmse:0.574971 test-rmse:0.721099 [12] train-rmse:0.543343 test-rmse:0.695099 [13] train-rmse:0.524634 test-rmse:0.677832 [14] train-rmse:0.514520 test-rmse:0.668357 [15] train-rmse:0.508937 test-rmse:0.665455 [16] train-rmse:0.505815 test-rmse:0.665300 [17] train-rmse:0.503878 test-rmse:0.665729 [18] train-rmse:0.502776 test-rmse:0.666025 [19] train-rmse:0.502157 test-rmse:0.665802 Stopping. Best iteration: [16] train-rmse:0.505815 test-rmse:0.665300 [1] train-rmse:8.468946 test-rmse:8.367333 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.949649 test-rmse:5.849577 [3] train-rmse:4.190648 test-rmse:4.088536 [4] train-rmse:2.965437 test-rmse:2.868553 [5] train-rmse:2.118037 test-rmse:2.027492 [6] train-rmse:1.538315 test-rmse:1.655643 [7] train-rmse:1.151336 test-rmse:1.269305 [8] train-rmse:0.897815 test-rmse:1.148503 [9] train-rmse:0.734588 test-rmse:1.069563 [10] train-rmse:0.636776 test-rmse:1.038559 [11] train-rmse:0.580524 test-rmse:1.026465 [12] train-rmse:0.549145 test-rmse:1.023694 [13] train-rmse:0.529627 test-rmse:1.025718 [14] train-rmse:0.520096 test-rmse:1.027397 [15] train-rmse:0.515071 test-rmse:1.020762 [16] train-rmse:0.511308 test-rmse:1.016146 [17] train-rmse:0.509524 test-rmse:1.009956 [18] train-rmse:0.508150 test-rmse:1.006066 [19] train-rmse:0.507424 test-rmse:1.003249 [20] train-rmse:0.507023 test-rmse:1.001503 [1] train-rmse:8.445681 test-rmse:8.514492 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.934694 test-rmse:6.000863 [3] train-rmse:4.182523 test-rmse:4.244866 [4] train-rmse:2.964906 test-rmse:3.021727 [5] train-rmse:2.125098 test-rmse:2.170028 [6] train-rmse:1.552744 test-rmse:1.557497 [7] train-rmse:1.171429 test-rmse:1.144796 [8] train-rmse:0.922810 test-rmse:0.881918 [9] train-rmse:0.767582 test-rmse:0.721032 [10] train-rmse:0.677326 test-rmse:0.638834 [11] train-rmse:0.626978 test-rmse:0.598808 [12] train-rmse:0.599746 test-rmse:0.588110 [13] train-rmse:0.583062 test-rmse:0.582564 [14] train-rmse:0.574415 test-rmse:0.583153 [15] train-rmse:0.569598 test-rmse:0.586412 [16] train-rmse:0.567002 test-rmse:0.592424 Stopping. Best iteration: [13] train-rmse:0.583062 test-rmse:0.582564
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.903547 test-rmse:9.415962 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.305658 test-rmse:6.822113 [3] train-rmse:4.504681 test-rmse:4.981092 [4] train-rmse:3.266255 test-rmse:3.714057 [5] train-rmse:2.419899 test-rmse:2.888823 [6] train-rmse:1.855062 test-rmse:2.434431 [7] train-rmse:1.489132 test-rmse:2.198516 [8] train-rmse:1.265660 test-rmse:2.083980 [9] train-rmse:1.112596 test-rmse:2.037294 [10] train-rmse:1.010037 test-rmse:2.019031 [11] train-rmse:0.946724 test-rmse:2.018782 [12] train-rmse:0.889866 test-rmse:1.989026 [13] train-rmse:0.855694 test-rmse:1.949443 [14] train-rmse:0.822821 test-rmse:1.964901 [15] train-rmse:0.808136 test-rmse:1.949441 [16] train-rmse:0.790875 test-rmse:1.945398 [17] train-rmse:0.777464 test-rmse:1.954233 [18] train-rmse:0.759406 test-rmse:1.956682 [19] train-rmse:0.751118 test-rmse:1.945487 Stopping. Best iteration: [16] train-rmse:0.790875 test-rmse:1.945398 [1] train-rmse:9.034137 test-rmse:8.657714 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.407408 test-rmse:6.013096 [3] train-rmse:4.588293 test-rmse:4.231235 [4] train-rmse:3.341739 test-rmse:2.996071 [5] train-rmse:2.483130 test-rmse:2.175344 [6] train-rmse:1.910310 test-rmse:1.639413 [7] train-rmse:1.531875 test-rmse:1.322649 [8] train-rmse:1.284200 test-rmse:1.150767 [9] train-rmse:1.124385 test-rmse:1.019266 [10] train-rmse:1.016190 test-rmse:0.976917 [11] train-rmse:0.949079 test-rmse:0.962533 [12] train-rmse:0.907562 test-rmse:0.968941 [13] train-rmse:0.878559 test-rmse:0.968714 [14] train-rmse:0.861678 test-rmse:0.988216 Stopping. Best iteration: [11] train-rmse:0.949079 test-rmse:0.962533 [1] train-rmse:9.019199 test-rmse:8.733879 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.393248 test-rmse:6.101609 [3] train-rmse:4.573199 test-rmse:4.322342 [4] train-rmse:3.324211 test-rmse:3.097361 [5] train-rmse:2.466624 test-rmse:2.234753 [6] train-rmse:1.891844 test-rmse:1.663629 [7] train-rmse:1.513008 test-rmse:1.365038 [8] train-rmse:1.261931 test-rmse:1.184896 [9] train-rmse:1.092243 test-rmse:1.117834 [10] train-rmse:0.989587 test-rmse:1.087488 [11] train-rmse:0.923492 test-rmse:1.065796 [12] train-rmse:0.872369 test-rmse:1.062668 [13] train-rmse:0.837337 test-rmse:1.079693 [14] train-rmse:0.821431 test-rmse:1.082953 [15] train-rmse:0.803417 test-rmse:1.088839 Stopping. Best iteration: [12] train-rmse:0.872369 test-rmse:1.062668 [1] train-rmse:8.980062 test-rmse:8.975072 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.363535 test-rmse:6.358956 [3] train-rmse:4.549993 test-rmse:4.527503 [4] train-rmse:3.302990 test-rmse:3.335969 [5] train-rmse:2.436646 test-rmse:2.550417 [6] train-rmse:1.852690 test-rmse:2.050954 [7] train-rmse:1.465558 test-rmse:1.716595 [8] train-rmse:1.210484 test-rmse:1.569025 [9] train-rmse:1.051180 test-rmse:1.484202 [10] train-rmse:0.953475 test-rmse:1.414945 [11] train-rmse:0.877703 test-rmse:1.405072 [12] train-rmse:0.828382 test-rmse:1.394937 [13] train-rmse:0.799330 test-rmse:1.375825 [14] train-rmse:0.773628 test-rmse:1.366931 [15] train-rmse:0.754081 test-rmse:1.362687 [16] train-rmse:0.741632 test-rmse:1.364412 [17] train-rmse:0.731549 test-rmse:1.364560 [18] train-rmse:0.723087 test-rmse:1.368413 Stopping. Best iteration: [15] train-rmse:0.754081 test-rmse:1.362687 [1] train-rmse:8.958695 test-rmse:9.091647 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.344325 test-rmse:6.488579 [3] train-rmse:4.527541 test-rmse:4.653090 [4] train-rmse:3.276180 test-rmse:3.410359 [5] train-rmse:2.424765 test-rmse:2.530899 [6] train-rmse:1.857964 test-rmse:1.944269 [7] train-rmse:1.481206 test-rmse:1.644673 [8] train-rmse:1.233629 test-rmse:1.474824 [9] train-rmse:1.085380 test-rmse:1.397994 [10] train-rmse:0.991078 test-rmse:1.358930 [11] train-rmse:0.936178 test-rmse:1.339837 [12] train-rmse:0.895573 test-rmse:1.343616 [13] train-rmse:0.872772 test-rmse:1.332446 [14] train-rmse:0.858608 test-rmse:1.330293 [15] train-rmse:0.848559 test-rmse:1.328691 [16] train-rmse:0.843308 test-rmse:1.319267 [17] train-rmse:0.834440 test-rmse:1.328349 [18] train-rmse:0.816554 test-rmse:1.339023 [19] train-rmse:0.814243 test-rmse:1.335710 Stopping. Best iteration: [16] train-rmse:0.843308 test-rmse:1.319267
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.772994 test-rmse:8.727715 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.158141 test-rmse:6.108894 [3] train-rmse:4.333283 test-rmse:4.283670 [4] train-rmse:3.064250 test-rmse:3.010986 [5] train-rmse:2.187324 test-rmse:2.133005 [6] train-rmse:1.587622 test-rmse:1.518833 [7] train-rmse:1.187166 test-rmse:1.104258 [8] train-rmse:0.928923 test-rmse:0.831321 [9] train-rmse:0.769872 test-rmse:0.659024 [10] train-rmse:0.677432 test-rmse:0.560013 [11] train-rmse:0.625814 test-rmse:0.509150 [12] train-rmse:0.598633 test-rmse:0.482102 [13] train-rmse:0.584549 test-rmse:0.470059 [14] train-rmse:0.577198 test-rmse:0.466879 [15] train-rmse:0.573077 test-rmse:0.467604 [16] train-rmse:0.571062 test-rmse:0.467430 [17] train-rmse:0.570098 test-rmse:0.467709 Stopping. Best iteration: [14] train-rmse:0.577198 test-rmse:0.466879 [1] train-rmse:8.754809 test-rmse:8.840071 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.145440 test-rmse:6.234729 [3] train-rmse:4.324625 test-rmse:4.413615 [4] train-rmse:3.058285 test-rmse:3.132977 [5] train-rmse:2.183360 test-rmse:2.242804 [6] train-rmse:1.586149 test-rmse:1.626889 [7] train-rmse:1.187017 test-rmse:1.202979 [8] train-rmse:0.929683 test-rmse:0.922134 [9] train-rmse:0.771641 test-rmse:0.734825 [10] train-rmse:0.680079 test-rmse:0.616504 [11] train-rmse:0.629887 test-rmse:0.544979 [12] train-rmse:0.602561 test-rmse:0.500458 [13] train-rmse:0.588545 test-rmse:0.473172 [14] train-rmse:0.581265 test-rmse:0.459505 [15] train-rmse:0.577535 test-rmse:0.450855 [16] train-rmse:0.575760 test-rmse:0.447966 [17] train-rmse:0.574829 test-rmse:0.446497 [18] train-rmse:0.574330 test-rmse:0.445483 [19] train-rmse:0.573996 test-rmse:0.447954 [20] train-rmse:0.573734 test-rmse:0.447102 [1] train-rmse:8.780005 test-rmse:8.676898 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.160572 test-rmse:6.060785 [3] train-rmse:4.331573 test-rmse:4.242305 [4] train-rmse:3.058249 test-rmse:2.980414 [5] train-rmse:2.176549 test-rmse:2.108381 [6] train-rmse:1.572335 test-rmse:1.523619 [7] train-rmse:1.165452 test-rmse:1.148263 [8] train-rmse:0.899279 test-rmse:0.915240 [9] train-rmse:0.733285 test-rmse:0.776210 [10] train-rmse:0.635774 test-rmse:0.705746 [11] train-rmse:0.581719 test-rmse:0.672696 [12] train-rmse:0.552790 test-rmse:0.658297 [13] train-rmse:0.537507 test-rmse:0.653838 [14] train-rmse:0.529425 test-rmse:0.653256 [15] train-rmse:0.525521 test-rmse:0.653819 [16] train-rmse:0.523421 test-rmse:0.655985 [17] train-rmse:0.522287 test-rmse:0.657279 Stopping. Best iteration: [14] train-rmse:0.529425 test-rmse:0.653256 [1] train-rmse:8.757217 test-rmse:8.821856 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.146209 test-rmse:6.209285 [3] train-rmse:4.324059 test-rmse:4.373478 [4] train-rmse:3.056562 test-rmse:3.094205 [5] train-rmse:2.180420 test-rmse:2.215091 [6] train-rmse:1.581596 test-rmse:1.608525 [7] train-rmse:1.179950 test-rmse:1.192110 [8] train-rmse:0.919597 test-rmse:0.923395 [9] train-rmse:0.759720 test-rmse:0.756891 [10] train-rmse:0.666361 test-rmse:0.653290 [11] train-rmse:0.614938 test-rmse:0.594429 [12] train-rmse:0.587577 test-rmse:0.564112 [13] train-rmse:0.573445 test-rmse:0.545959 [14] train-rmse:0.566040 test-rmse:0.538109 [15] train-rmse:0.562264 test-rmse:0.534774 [16] train-rmse:0.560273 test-rmse:0.534157 [17] train-rmse:0.559263 test-rmse:0.534422 [18] train-rmse:0.558707 test-rmse:0.534595 [19] train-rmse:0.558428 test-rmse:0.535427 Stopping. Best iteration: [16] train-rmse:0.560273 test-rmse:0.534157 [1] train-rmse:8.765414 test-rmse:8.763140 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.149046 test-rmse:6.153929 [3] train-rmse:4.322199 test-rmse:4.329002 [4] train-rmse:3.049560 test-rmse:3.064731 [5] train-rmse:2.167054 test-rmse:2.195139 [6] train-rmse:1.561357 test-rmse:1.606973 [7] train-rmse:1.152848 test-rmse:1.226058 [8] train-rmse:0.885164 test-rmse:0.986834 [9] train-rmse:0.717991 test-rmse:0.853717 [10] train-rmse:0.618878 test-rmse:0.782204 [11] train-rmse:0.563488 test-rmse:0.748109 [12] train-rmse:0.533970 test-rmse:0.734193 [13] train-rmse:0.518630 test-rmse:0.729935 [14] train-rmse:0.510884 test-rmse:0.729441 [15] train-rmse:0.506993 test-rmse:0.730699 [16] train-rmse:0.505009 test-rmse:0.732024 [17] train-rmse:0.504009 test-rmse:0.733313 Stopping. Best iteration: [14] train-rmse:0.510884 test-rmse:0.729441
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.853333 test-rmse:8.970897 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.236065 test-rmse:6.341735 [3] train-rmse:4.412713 test-rmse:4.524371 [4] train-rmse:3.151860 test-rmse:3.264997 [5] train-rmse:2.291044 test-rmse:2.401442 [6] train-rmse:1.717374 test-rmse:1.805200 [7] train-rmse:1.349287 test-rmse:1.406617 [8] train-rmse:1.121639 test-rmse:1.145247 [9] train-rmse:0.986380 test-rmse:0.993421 [10] train-rmse:0.911390 test-rmse:0.911222 [11] train-rmse:0.870855 test-rmse:0.870630 [12] train-rmse:0.849852 test-rmse:0.851691 [13] train-rmse:0.839092 test-rmse:0.842175 [14] train-rmse:0.833567 test-rmse:0.837891 [15] train-rmse:0.830803 test-rmse:0.835120 [16] train-rmse:0.829315 test-rmse:0.832749 [17] train-rmse:0.828545 test-rmse:0.832935 [18] train-rmse:0.828153 test-rmse:0.833286 [19] train-rmse:0.827830 test-rmse:0.836030 Stopping. Best iteration: [16] train-rmse:0.829315 test-rmse:0.832749 [1] train-rmse:8.880577 test-rmse:8.770506 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.243843 test-rmse:6.156182 [3] train-rmse:4.407802 test-rmse:4.352135 [4] train-rmse:3.135027 test-rmse:3.116132 [5] train-rmse:2.262049 test-rmse:2.296056 [6] train-rmse:1.674693 test-rmse:1.777050 [7] train-rmse:1.292163 test-rmse:1.472161 [8] train-rmse:1.050264 test-rmse:1.303128 [9] train-rmse:0.905654 test-rmse:1.222219 [10] train-rmse:0.825220 test-rmse:1.190983 [11] train-rmse:0.781346 test-rmse:1.181477 [12] train-rmse:0.758617 test-rmse:1.182223 [13] train-rmse:0.746691 test-rmse:1.182435 [14] train-rmse:0.740593 test-rmse:1.186423 Stopping. Best iteration: [11] train-rmse:0.781346 test-rmse:1.181477 [1] train-rmse:8.877302 test-rmse:8.822814 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.252199 test-rmse:6.188328 [3] train-rmse:4.427522 test-rmse:4.371910 [4] train-rmse:3.168491 test-rmse:3.109013 [5] train-rmse:2.307656 test-rmse:2.219120 [6] train-rmse:1.734026 test-rmse:1.622362 [7] train-rmse:1.365839 test-rmse:1.230775 [8] train-rmse:1.140214 test-rmse:0.976981 [9] train-rmse:1.009798 test-rmse:0.821855 [10] train-rmse:0.936959 test-rmse:0.735950 [11] train-rmse:0.897416 test-rmse:0.692135 [12] train-rmse:0.877231 test-rmse:0.672993 [13] train-rmse:0.866603 test-rmse:0.666541 [14] train-rmse:0.861263 test-rmse:0.662926 [15] train-rmse:0.858262 test-rmse:0.663621 [16] train-rmse:0.856661 test-rmse:0.663459 [17] train-rmse:0.855906 test-rmse:0.664313 Stopping. Best iteration: [14] train-rmse:0.861263 test-rmse:0.662926 [1] train-rmse:8.874659 test-rmse:8.828800 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.246948 test-rmse:6.201752 [3] train-rmse:4.419708 test-rmse:4.398425 [4] train-rmse:3.154306 test-rmse:3.147179 [5] train-rmse:2.291303 test-rmse:2.284984 [6] train-rmse:1.712270 test-rmse:1.716081 [7] train-rmse:1.336756 test-rmse:1.352860 [8] train-rmse:1.105713 test-rmse:1.120341 [9] train-rmse:0.971592 test-rmse:0.989012 [10] train-rmse:0.897147 test-rmse:0.916803 [11] train-rmse:0.857283 test-rmse:0.886524 [12] train-rmse:0.836795 test-rmse:0.869769 [13] train-rmse:0.825674 test-rmse:0.863383 [14] train-rmse:0.820343 test-rmse:0.862732 [15] train-rmse:0.817475 test-rmse:0.865086 [16] train-rmse:0.815936 test-rmse:0.866348 [17] train-rmse:0.815163 test-rmse:0.867848 Stopping. Best iteration: [14] train-rmse:0.820343 test-rmse:0.862732 [1] train-rmse:8.855656 test-rmse:8.946814 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.234267 test-rmse:6.323648 [3] train-rmse:4.410062 test-rmse:4.467308 [4] train-rmse:3.149453 test-rmse:3.185079 [5] train-rmse:2.284978 test-rmse:2.308757 [6] train-rmse:1.709640 test-rmse:1.727813 [7] train-rmse:1.334680 test-rmse:1.355964 [8] train-rmse:1.106948 test-rmse:1.132493 [9] train-rmse:0.971659 test-rmse:1.006047 [10] train-rmse:0.896041 test-rmse:0.929735 [11] train-rmse:0.855500 test-rmse:0.895031 [12] train-rmse:0.834407 test-rmse:0.884435 [13] train-rmse:0.823474 test-rmse:0.884002 [14] train-rmse:0.817963 test-rmse:0.883658 [15] train-rmse:0.814973 test-rmse:0.889216 [16] train-rmse:0.813443 test-rmse:0.893389 [17] train-rmse:0.812576 test-rmse:0.897536 Stopping. Best iteration: [14] train-rmse:0.817963 test-rmse:0.883658
| borough | b_class_group | Feature | avg_gain |
|---|---|---|---|
| <int> | <chr> | <chr> | <dbl> |
| 1 | c | residentialunits_group | 0.757358755 |
| 1 | c | zipcode | 0.140020605 |
| 1 | c | commercialunits_group | 0.049926493 |
| 1 | c | address_encoded | 0.028185330 |
| 1 | c | building_clusters | 0.011996033 |
| 1 | c | highly_commercial | 0.010542091 |
| 1 | c | taxclass_present | 0.001970693 |
| 1 | d | zipcode | 0.412801458 |
| 1 | d | residentialunits_group | 0.326393154 |
| 1 | d | address_encoded | 0.121375830 |
| 1 | d | commercialunits_group | 0.049233132 |
| 1 | d | highly_commercial | 0.085550699 |
| 1 | d | building_clusters | 0.004645726 |
| 1 | r | zipcode | 0.582665639 |
| 1 | r | residentialunits_group | 0.166176841 |
| 1 | r | taxclass_present | 0.074203438 |
| 1 | r | address_encoded | 0.072758708 |
| 1 | r | highly_commercial | 0.040980391 |
| 1 | r | onlycommercial | 0.016170575 |
| 1 | r | building_clusters | 0.029986972 |
| 1 | r | commercialunits_group | 0.017057437 |
| 1 | other | zipcode | 0.873802014 |
| 1 | other | address_encoded | 0.035126682 |
| 1 | other | commercialunits_group | 0.037414453 |
| 1 | other | residentialunits_group | 0.012995322 |
| 1 | other | onlycommercial | 0.013501613 |
| 1 | other | highly_commercial | 0.011997400 |
| 1 | other | taxclass_present | 0.008146610 |
| 1 | other | building_clusters | 0.008769883 |
| 1 | a | zipcode | 0.705346617 |
| ... | ... | ... | ... |
| 5 | c | commercialunits_group | 0.0605096694 |
| 5 | c | address_encoded | 0.1144313744 |
| 5 | c | highly_commercial | 0.0039306713 |
| 5 | c | residentialunits_group | 0.0856861390 |
| 5 | d | address_encoded | 0.9285833696 |
| 5 | d | residentialunits_group | 0.0472062252 |
| 5 | d | zipcode | 0.0420645628 |
| 5 | r | zipcode | 0.5317613089 |
| 5 | r | residentialunits_group | 0.2053598045 |
| 5 | r | taxclass_present | 0.1138547564 |
| 5 | r | address_encoded | 0.1305827796 |
| 5 | r | highly_commercial | 0.0230516883 |
| 5 | other | zipcode | 0.3312580284 |
| 5 | other | address_encoded | 0.1668383453 |
| 5 | other | taxclass_present | 0.1722947561 |
| 5 | other | commercialunits_group | 0.1279657414 |
| 5 | other | building_clusters | 0.0649466063 |
| 5 | other | residentialunits_group | 0.0570513848 |
| 5 | other | onlycommercial | 0.0158629861 |
| 5 | other | highly_commercial | 0.0959643410 |
| 5 | a | building_clusters | 0.3722322731 |
| 5 | a | zipcode | 0.4563325877 |
| 5 | a | address_encoded | 0.1682983045 |
| 5 | a | highly_commercial | 0.0028528486 |
| 5 | a | commercialunits_group | 0.0004733102 |
| 5 | b | zipcode | 0.7372280518 |
| 5 | b | address_encoded | 0.2490459679 |
| 5 | b | building_clusters | 0.0102905813 |
| 5 | b | commercialunits_group | 0.0020774571 |
| 5 | b | highly_commercial | 0.0027361559 |
[1] "overall test rmse:"
options(repr.plot.width = 10, repr.plot.height = 5, repr.plot.res = 200) # for graph sizes
p1 = ggplot(data = pred_table_bb, aes(x = actual, y = pred, color = as.factor(chunk))) + geom_point() + geom_abline(intercept = 0, slope = 1)+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
p2 = ggplot(data = pred_table_bb[actual < 20000000], aes(x = actual, y = pred, color = as.factor(chunk))) + geom_point() + geom_abline(intercept = 0, slope = 1) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
grid.arrange(p1,p2, ncol = 2)
#imp_table_bb = imp_table_bb[order(-avg_gain)]
#imp_table_bb[, .SD[1], .(borough,b_class_group)][order(-avg_gain)]
dt_tree = imp_table_bb[,.(M = mean(avg_gain, na.rm = TRUE), N = .N) ,.(b_class_group,Feature)]
options(repr.plot.width = 8, repr.plot.height = 5, repr.plot.res = 200)
ggplot(dt_tree
, aes(area = M, fill = N, label = Feature,subgroup = b_class_group)) +
geom_treemap() +
geom_treemap_subgroup_border() +
geom_treemap_subgroup_text(place = "centre", grow = T, alpha = 0.5, colour =
"black", fontface = "italic", min.size = 0) +
geom_treemap_text(colour = "white", place = "topleft", reflow = T)
ggplot(data = pred_table_bb[actual < 20000000, .(actual,pred,chunk,borough = dt[saleprice <20000000]$borough, b_class_group = dt[saleprice <20000000]$b_class_group)],
aes(x = actual, y=pred, group = as.factor(chunk), color = as.factor(chunk))) +
geom_point() +
facet_grid(borough~b_class_group) +
geom_abline(intercept = 0, slope = 1) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5))
Warning message in as.data.table.list(jval, .named = NULL): "Item 4 has 57439 rows but longest item has 57445; recycled with remainder." Warning message in as.data.table.list(jval, .named = NULL): "Item 5 has 57439 rows but longest item has 57445; recycled with remainder."
options(repr.plot.width = 5, repr.plot.height = 3, repr.plot.res = 200) # for graph sizes
ggplot(data = pred_table_bb[, .(rmse= calc_rmse(pred,actual)),.(borough, b_class_group)],
aes(x = as.factor(borough), y = rmse, group = as.factor(b_class_group), color = as.factor(b_class_group))) + geom_point()
## Both feature lists work nice
#feature_list = c("zipcode","residentialunits_log","commercialunits_log","address_encoded","taxclassatpresent_encoded","grosssquarefeet")
feature_list = c( "zipcode","commercialunits_group","residentialunits_group","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
)
train_target = "saleprice_wo"
test_target = "saleprice"
fit_bb_wo = model_xgboost_partial_wo(feature_list,train_target,test_target,chunk_no = 5)
pred_table_bb_wo = fit_bb_wo[[1]]
imp_table_bb_wo = fit_bb_wo[[2]]
imp_table_bb_wo
print("overall test rmse:")
calc_rmse(pred_table_bb_wo$pred,pred_table_bb_wo$actual)
calc_rmse(pred_table_bb_wo[actual < 20000000]$pred,pred_table_bb_wo[actual < 20000000]$actual)
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:3839487.250000 test-rmse:9646397.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3118016.000000 test-rmse:9392546.000000 [3] train-rmse:2655727.250000 test-rmse:9277878.000000 [4] train-rmse:2360343.500000 test-rmse:9173697.000000 [5] train-rmse:2123894.500000 test-rmse:9087418.000000 [6] train-rmse:1978108.500000 test-rmse:9067817.000000 [7] train-rmse:1889824.000000 test-rmse:8997614.000000 [8] train-rmse:1823306.875000 test-rmse:8988496.000000 [9] train-rmse:1781965.625000 test-rmse:8938518.000000 [10] train-rmse:1740777.375000 test-rmse:8934626.000000 [11] train-rmse:1710796.625000 test-rmse:8927242.000000 [12] train-rmse:1690548.750000 test-rmse:8934539.000000 [13] train-rmse:1671599.750000 test-rmse:8933006.000000 [14] train-rmse:1632114.250000 test-rmse:8934201.000000 Stopping. Best iteration: [11] train-rmse:1710796.625000 test-rmse:8927242.000000 [1] train-rmse:3602025.750000 test-rmse:6864078.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2917661.750000 test-rmse:6429995.500000 [3] train-rmse:2479801.500000 test-rmse:6157076.500000 [4] train-rmse:2214084.500000 test-rmse:5945006.000000 [5] train-rmse:2031999.750000 test-rmse:5843527.500000 [6] train-rmse:1923874.500000 test-rmse:5738926.000000 [7] train-rmse:1839754.375000 test-rmse:5715792.500000 [8] train-rmse:1785069.875000 test-rmse:5669931.500000 [9] train-rmse:1761412.375000 test-rmse:5639880.500000 [10] train-rmse:1739269.625000 test-rmse:5620737.000000 [11] train-rmse:1722456.750000 test-rmse:5584616.500000 [12] train-rmse:1705450.250000 test-rmse:5579414.500000 [13] train-rmse:1693920.375000 test-rmse:5576176.000000 [14] train-rmse:1688382.125000 test-rmse:5567862.000000 [15] train-rmse:1660819.750000 test-rmse:5522093.500000 [16] train-rmse:1643775.000000 test-rmse:5477279.500000 [17] train-rmse:1635972.250000 test-rmse:5468151.500000 [18] train-rmse:1632707.000000 test-rmse:5478293.000000 [19] train-rmse:1625219.750000 test-rmse:5441801.500000 [20] train-rmse:1607735.875000 test-rmse:5426252.000000 [1] train-rmse:3483855.750000 test-rmse:8145523.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2837200.750000 test-rmse:7633918.500000 [3] train-rmse:2379862.250000 test-rmse:7429358.000000 [4] train-rmse:2089705.750000 test-rmse:7295483.500000 [5] train-rmse:1914614.625000 test-rmse:7199155.500000 [6] train-rmse:1798729.875000 test-rmse:7148537.000000 [7] train-rmse:1713069.875000 test-rmse:7082435.000000 [8] train-rmse:1658610.250000 test-rmse:7056864.000000 [9] train-rmse:1613850.125000 test-rmse:7043270.500000 [10] train-rmse:1583908.000000 test-rmse:7040161.500000 [11] train-rmse:1548484.625000 test-rmse:7060285.000000 [12] train-rmse:1523290.375000 test-rmse:7091606.500000 [13] train-rmse:1496195.875000 test-rmse:7111326.000000 Stopping. Best iteration: [10] train-rmse:1583908.000000 test-rmse:7040161.500000 [1] train-rmse:3607674.750000 test-rmse:5358752.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2928525.250000 test-rmse:4908678.500000 [3] train-rmse:2487327.750000 test-rmse:4593105.000000 [4] train-rmse:2196149.500000 test-rmse:4398357.500000 [5] train-rmse:2008711.500000 test-rmse:4312515.000000 [6] train-rmse:1863278.000000 test-rmse:4283599.500000 [7] train-rmse:1778391.250000 test-rmse:4250227.500000 [8] train-rmse:1726376.125000 test-rmse:4229554.000000 [9] train-rmse:1682792.000000 test-rmse:4226065.500000 [10] train-rmse:1651789.375000 test-rmse:4221011.000000 [11] train-rmse:1616878.375000 test-rmse:4227951.500000 [12] train-rmse:1594660.500000 test-rmse:4277469.000000 [13] train-rmse:1582316.500000 test-rmse:4327778.500000 Stopping. Best iteration: [10] train-rmse:1651789.375000 test-rmse:4221011.000000 [1] train-rmse:3824686.250000 test-rmse:2848851.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3095337.500000 test-rmse:2269502.000000 [3] train-rmse:2631124.000000 test-rmse:2042796.625000 [4] train-rmse:2303853.250000 test-rmse:1965535.375000 [5] train-rmse:2097413.750000 test-rmse:1919362.750000 [6] train-rmse:1970080.125000 test-rmse:1925649.125000 [7] train-rmse:1894207.125000 test-rmse:1961753.125000 [8] train-rmse:1834820.250000 test-rmse:1956980.750000 Stopping. Best iteration: [5] train-rmse:2097413.750000 test-rmse:1919362.750000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:2329885.000000 test-rmse:9563970.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2021557.750000 test-rmse:9361510.000000 [3] train-rmse:1844939.875000 test-rmse:9226762.000000 [4] train-rmse:1746099.625000 test-rmse:9132696.000000 [5] train-rmse:1687760.500000 test-rmse:9078155.000000 [6] train-rmse:1650971.875000 test-rmse:9039501.000000 [7] train-rmse:1627246.750000 test-rmse:9001986.000000 [8] train-rmse:1614953.000000 test-rmse:8990724.000000 [9] train-rmse:1607435.625000 test-rmse:8987804.000000 [10] train-rmse:1600124.000000 test-rmse:8992623.000000 [11] train-rmse:1594824.875000 test-rmse:8989001.000000 [12] train-rmse:1590546.375000 test-rmse:8993877.000000 Stopping. Best iteration: [9] train-rmse:1607435.625000 test-rmse:8987804.000000 [1] train-rmse:2363115.500000 test-rmse:19170032.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2054236.500000 test-rmse:19133892.000000 [3] train-rmse:1873967.125000 test-rmse:19038868.000000 [4] train-rmse:1774843.125000 test-rmse:18977016.000000 [5] train-rmse:1718949.750000 test-rmse:18948254.000000 [6] train-rmse:1684769.875000 test-rmse:18912510.000000 [7] train-rmse:1667734.000000 test-rmse:18899094.000000 [8] train-rmse:1648253.125000 test-rmse:18874956.000000 [9] train-rmse:1640279.625000 test-rmse:18833472.000000 [10] train-rmse:1633221.625000 test-rmse:18818044.000000 [11] train-rmse:1629435.375000 test-rmse:18807572.000000 [12] train-rmse:1624041.750000 test-rmse:18798724.000000 [13] train-rmse:1620478.000000 test-rmse:18800314.000000 [14] train-rmse:1614037.875000 test-rmse:18776906.000000 [15] train-rmse:1610781.000000 test-rmse:18771404.000000 [16] train-rmse:1609649.125000 test-rmse:18769622.000000 [17] train-rmse:1608195.375000 test-rmse:18765318.000000 [18] train-rmse:1607078.875000 test-rmse:18762750.000000 [19] train-rmse:1604366.000000 test-rmse:18743072.000000 [20] train-rmse:1599318.750000 test-rmse:18742456.000000 [1] train-rmse:2272377.000000 test-rmse:3352918.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1953016.500000 test-rmse:3201342.500000 [3] train-rmse:1733803.875000 test-rmse:3124966.250000 [4] train-rmse:1612278.750000 test-rmse:3077424.000000 [5] train-rmse:1536944.375000 test-rmse:3050676.000000 [6] train-rmse:1495431.625000 test-rmse:3033288.000000 [7] train-rmse:1468327.875000 test-rmse:3018703.000000 [8] train-rmse:1450631.750000 test-rmse:3014931.250000 [9] train-rmse:1441771.625000 test-rmse:3011755.750000 [10] train-rmse:1433302.125000 test-rmse:3002195.750000 [11] train-rmse:1429640.000000 test-rmse:3000858.250000 [12] train-rmse:1424562.375000 test-rmse:2996515.000000 [13] train-rmse:1422874.625000 test-rmse:2996426.500000 [14] train-rmse:1420921.625000 test-rmse:2994281.500000 [15] train-rmse:1417640.375000 test-rmse:2993432.000000 [16] train-rmse:1415099.750000 test-rmse:2994951.500000 [17] train-rmse:1413233.750000 test-rmse:2995750.500000 [18] train-rmse:1411698.375000 test-rmse:2991246.750000 [19] train-rmse:1410229.375000 test-rmse:2990377.500000 [20] train-rmse:1409597.625000 test-rmse:2990296.750000 [1] train-rmse:2344181.250000 test-rmse:2294166.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2013022.000000 test-rmse:2141620.250000 [3] train-rmse:1800177.125000 test-rmse:2115178.250000 [4] train-rmse:1668680.750000 test-rmse:2136783.250000 [5] train-rmse:1597067.875000 test-rmse:2179158.250000 [6] train-rmse:1551979.250000 test-rmse:2208821.750000 Stopping. Best iteration: [3] train-rmse:1800177.125000 test-rmse:2115178.250000 [1] train-rmse:2267709.250000 test-rmse:6751553.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1979890.750000 test-rmse:6507449.500000 [3] train-rmse:1804483.875000 test-rmse:6411497.000000 [4] train-rmse:1704677.875000 test-rmse:6352002.500000 [5] train-rmse:1635174.375000 test-rmse:6270823.500000 [6] train-rmse:1598596.125000 test-rmse:6235463.000000 [7] train-rmse:1574752.625000 test-rmse:6206316.500000 [8] train-rmse:1562065.500000 test-rmse:6193690.500000 [9] train-rmse:1548749.875000 test-rmse:6148672.000000 [10] train-rmse:1543282.500000 test-rmse:6143989.500000 [11] train-rmse:1537403.750000 test-rmse:6136367.000000 [12] train-rmse:1533812.000000 test-rmse:6135532.000000 [13] train-rmse:1530610.875000 test-rmse:6131943.000000 [14] train-rmse:1521605.250000 test-rmse:6142941.000000 [15] train-rmse:1516275.500000 test-rmse:6156039.500000 [16] train-rmse:1511828.000000 test-rmse:6164298.000000 Stopping. Best iteration: [13] train-rmse:1530610.875000 test-rmse:6131943.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:3330921.250000 test-rmse:5642429.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:2993838.750000 test-rmse:5317818.000000 [3] train-rmse:2800290.750000 test-rmse:5141268.500000 [4] train-rmse:2686863.250000 test-rmse:5049857.500000 [5] train-rmse:2617315.000000 test-rmse:5031805.500000 [6] train-rmse:2579498.250000 test-rmse:5015712.000000 [7] train-rmse:2560285.250000 test-rmse:4983759.500000 [8] train-rmse:2540314.500000 test-rmse:4974679.000000 [9] train-rmse:2528287.250000 test-rmse:4965832.500000 [10] train-rmse:2520369.500000 test-rmse:4958357.500000 [11] train-rmse:2497315.500000 test-rmse:4957125.500000 [12] train-rmse:2494314.750000 test-rmse:4950861.000000 [13] train-rmse:2491937.250000 test-rmse:4952371.000000 [14] train-rmse:2486547.750000 test-rmse:4948675.500000 [15] train-rmse:2482059.500000 test-rmse:4947968.500000 [16] train-rmse:2478795.250000 test-rmse:4948869.000000 [17] train-rmse:2463706.250000 test-rmse:4948000.000000 [18] train-rmse:2461379.750000 test-rmse:4950708.500000 Stopping. Best iteration: [15] train-rmse:2482059.500000 test-rmse:4947968.500000 [1] train-rmse:3397153.250000 test-rmse:5453558.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3032779.000000 test-rmse:5220809.500000 [3] train-rmse:2829580.000000 test-rmse:5092452.000000 [4] train-rmse:2720691.500000 test-rmse:5000993.000000 [5] train-rmse:2648021.250000 test-rmse:4952301.500000 [6] train-rmse:2609762.750000 test-rmse:4913740.500000 [7] train-rmse:2591382.250000 test-rmse:4876211.000000 [8] train-rmse:2580498.250000 test-rmse:4864848.500000 [9] train-rmse:2563503.250000 test-rmse:4862853.500000 [10] train-rmse:2555280.500000 test-rmse:4850933.500000 [11] train-rmse:2551333.500000 test-rmse:4841140.000000 [12] train-rmse:2547574.750000 test-rmse:4833527.500000 [13] train-rmse:2531927.750000 test-rmse:4828056.500000 [14] train-rmse:2529212.000000 test-rmse:4824918.500000 [15] train-rmse:2523696.500000 test-rmse:4829755.500000 [16] train-rmse:2522263.250000 test-rmse:4828445.500000 [17] train-rmse:2521540.000000 test-rmse:4825228.500000 Stopping. Best iteration: [14] train-rmse:2529212.000000 test-rmse:4824918.500000 [1] train-rmse:3462394.500000 test-rmse:8242791.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3082193.750000 test-rmse:8120516.000000 [3] train-rmse:2870173.750000 test-rmse:8050668.500000 [4] train-rmse:2735364.500000 test-rmse:7981087.000000 [5] train-rmse:2659183.000000 test-rmse:7928720.000000 [6] train-rmse:2619991.250000 test-rmse:7921414.000000 [7] train-rmse:2598727.000000 test-rmse:7914529.000000 [8] train-rmse:2581579.000000 test-rmse:7869546.500000 [9] train-rmse:2570342.000000 test-rmse:7866656.500000 [10] train-rmse:2561874.500000 test-rmse:7867319.000000 [11] train-rmse:2555116.500000 test-rmse:7867382.500000 [12] train-rmse:2544364.000000 test-rmse:7870667.500000 Stopping. Best iteration: [9] train-rmse:2570342.000000 test-rmse:7866656.500000 [1] train-rmse:3405449.500000 test-rmse:5916559.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3046304.750000 test-rmse:5660512.500000 [3] train-rmse:2846861.500000 test-rmse:5534847.000000 [4] train-rmse:2722484.750000 test-rmse:5444879.000000 [5] train-rmse:2659107.750000 test-rmse:5395961.000000 [6] train-rmse:2624359.500000 test-rmse:5375234.500000 [7] train-rmse:2605268.750000 test-rmse:5372514.000000 [8] train-rmse:2594463.000000 test-rmse:5366855.000000 [9] train-rmse:2580774.750000 test-rmse:5360295.500000 [10] train-rmse:2573015.000000 test-rmse:5359166.000000 [11] train-rmse:2567557.750000 test-rmse:5359048.500000 [12] train-rmse:2557422.250000 test-rmse:5346566.500000 [13] train-rmse:2553484.500000 test-rmse:5346840.500000 [14] train-rmse:2548802.750000 test-rmse:5340963.500000 [15] train-rmse:2541519.250000 test-rmse:5339267.000000 [16] train-rmse:2539052.750000 test-rmse:5340973.000000 [17] train-rmse:2538106.500000 test-rmse:5342331.000000 [18] train-rmse:2535157.750000 test-rmse:5338736.500000 [19] train-rmse:2533778.500000 test-rmse:5339759.000000 [20] train-rmse:2528917.500000 test-rmse:5336942.500000 [1] train-rmse:3509593.000000 test-rmse:4722505.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3118812.250000 test-rmse:4583770.000000 [3] train-rmse:2901818.750000 test-rmse:4495535.500000 [4] train-rmse:2769811.750000 test-rmse:4512720.000000 [5] train-rmse:2693967.750000 test-rmse:4500829.000000 [6] train-rmse:2649493.500000 test-rmse:4645371.500000 Stopping. Best iteration: [3] train-rmse:2901818.750000 test-rmse:4495535.500000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:7869727.000000 test-rmse:168655312.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6344750.500000 test-rmse:167802688.000000 [3] train-rmse:5281328.000000 test-rmse:167231520.000000 [4] train-rmse:4652542.000000 test-rmse:166796896.000000 [5] train-rmse:4198321.000000 test-rmse:166516160.000000 [6] train-rmse:3863143.000000 test-rmse:166284688.000000 [7] train-rmse:3596122.000000 test-rmse:166107744.000000 [8] train-rmse:3417043.500000 test-rmse:165991744.000000 [9] train-rmse:3283428.750000 test-rmse:165831248.000000 [10] train-rmse:3206112.000000 test-rmse:165716992.000000 [11] train-rmse:3143363.500000 test-rmse:165634672.000000 [12] train-rmse:3095888.750000 test-rmse:165620480.000000 [13] train-rmse:3031491.750000 test-rmse:165606384.000000 [14] train-rmse:2983566.750000 test-rmse:165595584.000000 [15] train-rmse:2927927.250000 test-rmse:165580496.000000 [16] train-rmse:2830979.250000 test-rmse:165506144.000000 [17] train-rmse:2801094.750000 test-rmse:165487856.000000 [18] train-rmse:2771360.250000 test-rmse:165489600.000000 [19] train-rmse:2717284.250000 test-rmse:165472016.000000 [20] train-rmse:2680581.000000 test-rmse:165477648.000000 [1] train-rmse:8256673.000000 test-rmse:49205052.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6611951.500000 test-rmse:48171412.000000 [3] train-rmse:5499488.500000 test-rmse:47389124.000000 [4] train-rmse:4779006.000000 test-rmse:46913264.000000 [5] train-rmse:4303091.000000 test-rmse:46538876.000000 [6] train-rmse:3996540.250000 test-rmse:46336336.000000 [7] train-rmse:3783654.750000 test-rmse:46190292.000000 [8] train-rmse:3586146.750000 test-rmse:46149976.000000 [9] train-rmse:3462708.250000 test-rmse:46104484.000000 [10] train-rmse:3374060.500000 test-rmse:46096712.000000 [11] train-rmse:3259354.000000 test-rmse:46056528.000000 [12] train-rmse:3187218.750000 test-rmse:46017880.000000 [13] train-rmse:3147640.500000 test-rmse:46050944.000000 [14] train-rmse:3111084.500000 test-rmse:46047176.000000 [15] train-rmse:3086585.750000 test-rmse:46032224.000000 Stopping. Best iteration: [12] train-rmse:3187218.750000 test-rmse:46017880.000000 [1] train-rmse:8228949.000000 test-rmse:269496160.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6595285.000000 test-rmse:268989408.000000 [3] train-rmse:5531427.500000 test-rmse:268588576.000000 [4] train-rmse:4817068.000000 test-rmse:268310112.000000 [5] train-rmse:4332763.500000 test-rmse:268148320.000000 [6] train-rmse:4022352.250000 test-rmse:268029344.000000 [7] train-rmse:3790194.750000 test-rmse:267896880.000000 [8] train-rmse:3625943.000000 test-rmse:267835952.000000 [9] train-rmse:3495136.500000 test-rmse:267690480.000000 [10] train-rmse:3425512.000000 test-rmse:267583376.000000 [11] train-rmse:3381846.250000 test-rmse:267499008.000000 [12] train-rmse:3337474.000000 test-rmse:267509344.000000 [13] train-rmse:3276071.000000 test-rmse:267467456.000000 [14] train-rmse:3240043.500000 test-rmse:267470208.000000 [15] train-rmse:3215904.000000 test-rmse:267471120.000000 [16] train-rmse:3167794.250000 test-rmse:267459280.000000 [17] train-rmse:3151144.250000 test-rmse:267462800.000000 [18] train-rmse:3115895.250000 test-rmse:267484832.000000 [19] train-rmse:3093071.250000 test-rmse:267476864.000000 Stopping. Best iteration: [16] train-rmse:3167794.250000 test-rmse:267459280.000000 [1] train-rmse:8925397.000000 test-rmse:36558272.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7153770.000000 test-rmse:36397552.000000 [3] train-rmse:5941971.000000 test-rmse:36197840.000000 [4] train-rmse:5109659.000000 test-rmse:36183848.000000 [5] train-rmse:4654399.500000 test-rmse:36053028.000000 [6] train-rmse:4375866.000000 test-rmse:36078444.000000 [7] train-rmse:4151227.500000 test-rmse:36026064.000000 [8] train-rmse:3990347.750000 test-rmse:36041948.000000 [9] train-rmse:3885274.000000 test-rmse:36088504.000000 [10] train-rmse:3736684.250000 test-rmse:36139320.000000 Stopping. Best iteration: [7] train-rmse:4151227.500000 test-rmse:36026064.000000 [1] train-rmse:7976355.000000 test-rmse:34015232.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6427651.000000 test-rmse:32843476.000000 [3] train-rmse:5380964.500000 test-rmse:32064740.000000 [4] train-rmse:4659554.500000 test-rmse:31810206.000000 [5] train-rmse:4179740.500000 test-rmse:31469746.000000 [6] train-rmse:3854211.000000 test-rmse:31287492.000000 [7] train-rmse:3632315.000000 test-rmse:31078968.000000 [8] train-rmse:3474925.250000 test-rmse:31039852.000000 [9] train-rmse:3392053.500000 test-rmse:31045738.000000 [10] train-rmse:3333149.000000 test-rmse:31021276.000000 [11] train-rmse:3296023.500000 test-rmse:31013650.000000 [12] train-rmse:3207271.750000 test-rmse:30968528.000000 [13] train-rmse:3143709.000000 test-rmse:30957616.000000 [14] train-rmse:3066071.750000 test-rmse:30941038.000000 [15] train-rmse:3023304.250000 test-rmse:30929174.000000 [16] train-rmse:2958287.000000 test-rmse:30920462.000000 [17] train-rmse:2951269.000000 test-rmse:30904354.000000 [18] train-rmse:2944448.500000 test-rmse:30904864.000000 [19] train-rmse:2929502.000000 test-rmse:30905280.000000 [20] train-rmse:2877980.000000 test-rmse:30894954.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:7148730.000000 test-rmse:9216487.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5841270.500000 test-rmse:8117133.500000 [3] train-rmse:4873362.000000 test-rmse:7721318.500000 [4] train-rmse:4224745.500000 test-rmse:7500595.500000 [5] train-rmse:3786651.000000 test-rmse:7319108.500000 [6] train-rmse:3524337.000000 test-rmse:7173468.500000 [7] train-rmse:3366028.250000 test-rmse:7076233.500000 [8] train-rmse:3271422.250000 test-rmse:6891596.500000 [9] train-rmse:3213586.500000 test-rmse:6853543.500000 [10] train-rmse:3177854.500000 test-rmse:6823176.500000 [11] train-rmse:3155766.500000 test-rmse:6798836.500000 [12] train-rmse:3141490.000000 test-rmse:6740826.000000 [13] train-rmse:3132179.750000 test-rmse:6724990.500000 [14] train-rmse:3126026.750000 test-rmse:6712161.000000 [15] train-rmse:3121955.000000 test-rmse:6701737.500000 [16] train-rmse:3119257.250000 test-rmse:6693248.000000 [17] train-rmse:3117406.250000 test-rmse:6686316.500000 [18] train-rmse:3116045.250000 test-rmse:6675722.500000 [19] train-rmse:3115107.250000 test-rmse:6665600.000000 [20] train-rmse:3114445.250000 test-rmse:6659518.000000 [1] train-rmse:7963658.000000 test-rmse:1593642.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6300184.000000 test-rmse:1492525.625000 [3] train-rmse:5164838.500000 test-rmse:1553998.000000 [4] train-rmse:4446926.000000 test-rmse:1644147.250000 [5] train-rmse:3980724.750000 test-rmse:1817515.375000 Stopping. Best iteration: [2] train-rmse:6300184.000000 test-rmse:1492525.625000 [1] train-rmse:6538009.500000 test-rmse:16203406.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5136829.000000 test-rmse:14654148.000000 [3] train-rmse:4190947.500000 test-rmse:13726132.000000 [4] train-rmse:3571253.500000 test-rmse:13009727.000000 [5] train-rmse:3153751.000000 test-rmse:12601419.000000 [6] train-rmse:2862604.250000 test-rmse:12455366.000000 [7] train-rmse:2672566.750000 test-rmse:12357727.000000 [8] train-rmse:2525439.000000 test-rmse:12257764.000000 [9] train-rmse:2427484.250000 test-rmse:12229783.000000 [10] train-rmse:2351331.750000 test-rmse:12215677.000000 [11] train-rmse:2300454.250000 test-rmse:12211285.000000 [12] train-rmse:2266617.750000 test-rmse:12212191.000000 [13] train-rmse:2245500.000000 test-rmse:12222113.000000 [14] train-rmse:2229313.250000 test-rmse:12228043.000000 Stopping. Best iteration: [11] train-rmse:2300454.250000 test-rmse:12211285.000000 [1] train-rmse:7029001.500000 test-rmse:8122968.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5561297.500000 test-rmse:7018070.500000 [3] train-rmse:4560024.500000 test-rmse:6301628.000000 [4] train-rmse:3914873.500000 test-rmse:5926374.000000 [5] train-rmse:3493241.750000 test-rmse:5628458.500000 [6] train-rmse:3242160.250000 test-rmse:5584723.000000 [7] train-rmse:3051270.000000 test-rmse:5503842.500000 [8] train-rmse:2907246.000000 test-rmse:5554456.000000 [9] train-rmse:2809419.750000 test-rmse:5610414.000000 [10] train-rmse:2741994.500000 test-rmse:5640131.000000 Stopping. Best iteration: [7] train-rmse:3051270.000000 test-rmse:5503842.500000 [1] train-rmse:7056012.000000 test-rmse:7007014.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5620416.500000 test-rmse:5865347.000000 [3] train-rmse:4659953.500000 test-rmse:5121764.500000 [4] train-rmse:4027321.750000 test-rmse:4560446.500000 [5] train-rmse:3631890.750000 test-rmse:4324033.500000 [6] train-rmse:3352017.250000 test-rmse:4175790.000000 [7] train-rmse:3181431.000000 test-rmse:4099827.250000 [8] train-rmse:3084985.750000 test-rmse:4008002.500000 [9] train-rmse:3025761.000000 test-rmse:3974687.250000 [10] train-rmse:2961796.750000 test-rmse:3955716.000000 [11] train-rmse:2912962.750000 test-rmse:3945181.750000 [12] train-rmse:2879080.000000 test-rmse:3939599.250000 [13] train-rmse:2855057.500000 test-rmse:3932467.500000 [14] train-rmse:2837580.500000 test-rmse:3927813.250000 [15] train-rmse:2826524.500000 test-rmse:3926459.000000 [16] train-rmse:2818820.000000 test-rmse:3926579.000000 [17] train-rmse:2813165.500000 test-rmse:3926661.500000 [18] train-rmse:2809125.500000 test-rmse:3926750.500000 Stopping. Best iteration: [15] train-rmse:2826524.500000 test-rmse:3926459.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:4681717.500000 test-rmse:6960449.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3712466.250000 test-rmse:6293495.500000 [3] train-rmse:2998262.500000 test-rmse:5807638.500000 [4] train-rmse:2477385.000000 test-rmse:5454354.000000 [5] train-rmse:2106382.250000 test-rmse:4931710.000000 [6] train-rmse:1816261.000000 test-rmse:4923400.000000 [7] train-rmse:1610685.250000 test-rmse:4927109.500000 [8] train-rmse:1460926.625000 test-rmse:4937996.000000 [9] train-rmse:1359496.375000 test-rmse:4952413.000000 Stopping. Best iteration: [6] train-rmse:1816261.000000 test-rmse:4923400.000000 [1] train-rmse:5415799.000000 test-rmse:1419053.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4314899.000000 test-rmse:1177170.875000 [3] train-rmse:3595378.250000 test-rmse:1761903.125000 [4] train-rmse:3005128.000000 test-rmse:1506070.625000 [5] train-rmse:2576428.750000 test-rmse:1318662.250000 Stopping. Best iteration: [2] train-rmse:4314899.000000 test-rmse:1177170.875000 [1] train-rmse:5392540.500000 test-rmse:1950386.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:4309955.500000 test-rmse:1649911.000000 [3] train-rmse:3532104.750000 test-rmse:1473460.250000 [4] train-rmse:2988825.250000 test-rmse:1383574.000000 [5] train-rmse:2563869.000000 test-rmse:1279609.125000 [6] train-rmse:2268596.000000 test-rmse:1256744.500000 [7] train-rmse:2066382.500000 test-rmse:1209195.000000 [8] train-rmse:1920218.000000 test-rmse:1209364.125000 [9] train-rmse:1828150.875000 test-rmse:1188710.375000 [10] train-rmse:1757076.875000 test-rmse:1175728.250000 [11] train-rmse:1709322.125000 test-rmse:1162626.000000 [12] train-rmse:1672396.625000 test-rmse:1153047.500000 [13] train-rmse:1642528.875000 test-rmse:1159036.875000 [14] train-rmse:1621811.500000 test-rmse:1169154.125000 [15] train-rmse:1608350.250000 test-rmse:1174958.750000 Stopping. Best iteration: [12] train-rmse:1672396.625000 test-rmse:1153047.500000 [1] train-rmse:4252982.500000 test-rmse:7848956.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3452230.000000 test-rmse:6702025.500000 [3] train-rmse:2868736.000000 test-rmse:5800653.000000 [4] train-rmse:2452318.750000 test-rmse:5473692.500000 [5] train-rmse:2162689.250000 test-rmse:5070290.000000 [6] train-rmse:1947964.750000 test-rmse:4932280.000000 [7] train-rmse:1788269.750000 test-rmse:4819154.500000 [8] train-rmse:1672491.750000 test-rmse:4774554.000000 [9] train-rmse:1588310.125000 test-rmse:4742489.000000 [10] train-rmse:1527949.250000 test-rmse:4722033.500000 [11] train-rmse:1484367.875000 test-rmse:4721916.500000 [12] train-rmse:1453250.000000 test-rmse:4723873.500000 [13] train-rmse:1430668.000000 test-rmse:4733469.500000 [14] train-rmse:1414412.250000 test-rmse:4741965.500000 Stopping. Best iteration: [11] train-rmse:1484367.875000 test-rmse:4721916.500000 [1] train-rmse:4777935.000000 test-rmse:6936941.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:3798886.000000 test-rmse:6547519.500000 [3] train-rmse:3103361.500000 test-rmse:6294621.500000 [4] train-rmse:2609887.000000 test-rmse:6107394.000000 [5] train-rmse:2257632.500000 test-rmse:5978801.500000 [6] train-rmse:2040584.750000 test-rmse:5880481.000000 [7] train-rmse:1879146.375000 test-rmse:5820528.000000 [8] train-rmse:1781958.500000 test-rmse:5777225.000000 [9] train-rmse:1709553.250000 test-rmse:5746454.000000 [10] train-rmse:1661648.750000 test-rmse:5724305.500000 [11] train-rmse:1629739.125000 test-rmse:5712813.500000 [12] train-rmse:1608647.625000 test-rmse:5704293.500000 [13] train-rmse:1594659.875000 test-rmse:5695371.500000 [14] train-rmse:1585094.375000 test-rmse:5691013.000000 [15] train-rmse:1578629.375000 test-rmse:5686126.000000 [16] train-rmse:1574184.875000 test-rmse:5681631.000000 [17] train-rmse:1571186.500000 test-rmse:5680276.000000 [18] train-rmse:1569072.625000 test-rmse:5678577.000000 [19] train-rmse:1567619.125000 test-rmse:5674416.500000 [20] train-rmse:1566575.000000 test-rmse:5673305.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:981550.000000 test-rmse:4727806.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:745145.625000 test-rmse:4588755.500000 [3] train-rmse:587272.375000 test-rmse:4497045.500000 [4] train-rmse:484126.750000 test-rmse:4458812.500000 [5] train-rmse:416202.031250 test-rmse:4427210.500000 [6] train-rmse:374070.218750 test-rmse:4403776.000000 [7] train-rmse:346865.875000 test-rmse:4382269.500000 [8] train-rmse:328561.406250 test-rmse:4366250.500000 [9] train-rmse:319536.375000 test-rmse:4356498.000000 [10] train-rmse:312005.250000 test-rmse:4350602.500000 [11] train-rmse:305685.875000 test-rmse:4353409.500000 [12] train-rmse:302462.687500 test-rmse:4353071.000000 [13] train-rmse:299479.062500 test-rmse:4352748.500000 Stopping. Best iteration: [10] train-rmse:312005.250000 test-rmse:4350602.500000 [1] train-rmse:936073.312500 test-rmse:3153533.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:716439.187500 test-rmse:2904765.250000 [3] train-rmse:571993.000000 test-rmse:2741219.000000 [4] train-rmse:477391.312500 test-rmse:2647197.500000 [5] train-rmse:416306.468750 test-rmse:2566533.750000 [6] train-rmse:375988.062500 test-rmse:2504165.500000 [7] train-rmse:352240.968750 test-rmse:2459282.500000 [8] train-rmse:336815.687500 test-rmse:2426972.250000 [9] train-rmse:327284.062500 test-rmse:2410609.250000 [10] train-rmse:318831.093750 test-rmse:2399452.250000 [11] train-rmse:314326.375000 test-rmse:2393546.250000 [12] train-rmse:310881.281250 test-rmse:2386734.000000 [13] train-rmse:307919.406250 test-rmse:2381929.750000 [14] train-rmse:305569.406250 test-rmse:2378593.500000 [15] train-rmse:304420.531250 test-rmse:2373781.750000 [16] train-rmse:303448.156250 test-rmse:2368848.000000 [17] train-rmse:302690.125000 test-rmse:2366681.500000 [18] train-rmse:299520.250000 test-rmse:2366956.500000 [19] train-rmse:297696.156250 test-rmse:2367647.750000 [20] train-rmse:297074.375000 test-rmse:2365854.250000 [1] train-rmse:972383.937500 test-rmse:1782094.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:738555.250000 test-rmse:1567922.375000 [3] train-rmse:583335.250000 test-rmse:1410389.375000 [4] train-rmse:482878.343750 test-rmse:1320919.750000 [5] train-rmse:421502.812500 test-rmse:1263209.375000 [6] train-rmse:386472.218750 test-rmse:1229208.875000 [7] train-rmse:362861.781250 test-rmse:1209739.375000 [8] train-rmse:348571.437500 test-rmse:1191219.500000 [9] train-rmse:339109.812500 test-rmse:1181451.375000 [10] train-rmse:332635.687500 test-rmse:1170843.750000 [11] train-rmse:326748.187500 test-rmse:1165914.500000 [12] train-rmse:324492.687500 test-rmse:1158136.750000 [13] train-rmse:322330.750000 test-rmse:1155689.500000 [14] train-rmse:319710.250000 test-rmse:1153310.500000 [15] train-rmse:317991.812500 test-rmse:1149705.750000 [16] train-rmse:316854.906250 test-rmse:1148586.500000 [17] train-rmse:315432.000000 test-rmse:1150132.625000 [18] train-rmse:314335.500000 test-rmse:1149691.750000 [19] train-rmse:313553.562500 test-rmse:1150461.000000 Stopping. Best iteration: [16] train-rmse:316854.906250 test-rmse:1148586.500000 [1] train-rmse:1021824.187500 test-rmse:1795138.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:776719.437500 test-rmse:1649407.125000 [3] train-rmse:611582.375000 test-rmse:1554998.250000 [4] train-rmse:507829.562500 test-rmse:1489893.750000 [5] train-rmse:438513.718750 test-rmse:1453139.750000 [6] train-rmse:395786.718750 test-rmse:1425835.875000 [7] train-rmse:370487.687500 test-rmse:1411984.875000 [8] train-rmse:353684.750000 test-rmse:1396308.875000 [9] train-rmse:342987.531250 test-rmse:1385458.000000 [10] train-rmse:335797.406250 test-rmse:1379692.750000 [11] train-rmse:330390.843750 test-rmse:1373355.500000 [12] train-rmse:326373.031250 test-rmse:1366060.000000 [13] train-rmse:321454.562500 test-rmse:1362550.250000 [14] train-rmse:318331.562500 test-rmse:1359714.875000 [15] train-rmse:316084.656250 test-rmse:1359985.500000 [16] train-rmse:312828.437500 test-rmse:1359838.125000 [17] train-rmse:311880.531250 test-rmse:1357929.500000 [18] train-rmse:309695.218750 test-rmse:1356466.375000 [19] train-rmse:308694.062500 test-rmse:1355678.375000 [20] train-rmse:307432.531250 test-rmse:1354608.500000 [1] train-rmse:1040753.625000 test-rmse:1114059.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:785571.187500 test-rmse:962257.875000 [3] train-rmse:615742.187500 test-rmse:868289.125000 [4] train-rmse:508065.125000 test-rmse:812221.937500 [5] train-rmse:436694.125000 test-rmse:778384.125000 [6] train-rmse:388872.875000 test-rmse:762601.312500 [7] train-rmse:362577.843750 test-rmse:753309.312500 [8] train-rmse:346526.250000 test-rmse:746785.375000 [9] train-rmse:336117.656250 test-rmse:745237.750000 [10] train-rmse:329799.843750 test-rmse:738850.625000 [11] train-rmse:323508.750000 test-rmse:736973.875000 [12] train-rmse:318069.156250 test-rmse:733503.062500 [13] train-rmse:313212.906250 test-rmse:732636.937500 [14] train-rmse:309690.906250 test-rmse:731953.437500 [15] train-rmse:307205.375000 test-rmse:732021.625000 [16] train-rmse:304873.500000 test-rmse:733579.875000 [17] train-rmse:303115.593750 test-rmse:733481.625000 Stopping. Best iteration: [14] train-rmse:309690.906250 test-rmse:731953.437500
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:514119.687500 test-rmse:4256450.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:406137.531250 test-rmse:4119713.000000 [3] train-rmse:331605.218750 test-rmse:4027922.750000 [4] train-rmse:287966.375000 test-rmse:3963963.250000 [5] train-rmse:257262.656250 test-rmse:3917643.000000 [6] train-rmse:238987.984375 test-rmse:3884563.750000 [7] train-rmse:228470.421875 test-rmse:3861040.000000 [8] train-rmse:222333.078125 test-rmse:3843743.750000 [9] train-rmse:218767.750000 test-rmse:3831249.250000 [10] train-rmse:216532.578125 test-rmse:3823982.500000 [11] train-rmse:215174.375000 test-rmse:3818323.250000 [12] train-rmse:214339.703125 test-rmse:3813902.750000 [13] train-rmse:213818.015625 test-rmse:3810488.000000 [14] train-rmse:213484.875000 test-rmse:3808286.500000 [15] train-rmse:213270.015625 test-rmse:3806524.250000 [16] train-rmse:213131.437500 test-rmse:3805257.750000 [17] train-rmse:213039.859375 test-rmse:3803685.750000 [18] train-rmse:212979.078125 test-rmse:3802955.250000 [19] train-rmse:212934.375000 test-rmse:3802110.750000 [20] train-rmse:212904.531250 test-rmse:3801556.000000 [1] train-rmse:521907.437500 test-rmse:6939233.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:420303.843750 test-rmse:6827733.000000 [3] train-rmse:343976.437500 test-rmse:6772997.500000 [4] train-rmse:293130.812500 test-rmse:6776337.000000 [5] train-rmse:259527.968750 test-rmse:6744641.000000 [6] train-rmse:241354.500000 test-rmse:6739832.500000 [7] train-rmse:227712.484375 test-rmse:6723669.000000 [8] train-rmse:219490.156250 test-rmse:6712387.500000 [9] train-rmse:214695.812500 test-rmse:6705135.000000 [10] train-rmse:211533.250000 test-rmse:6699262.000000 [11] train-rmse:209500.328125 test-rmse:6699328.000000 [12] train-rmse:208195.812500 test-rmse:6699619.500000 [13] train-rmse:207337.203125 test-rmse:6700664.000000 Stopping. Best iteration: [10] train-rmse:211533.250000 test-rmse:6699262.000000 [1] train-rmse:621275.562500 test-rmse:242077.812500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:488598.687500 test-rmse:203764.234375 [3] train-rmse:404101.593750 test-rmse:182268.109375 [4] train-rmse:344715.125000 test-rmse:170970.718750 [5] train-rmse:307238.937500 test-rmse:165582.093750 [6] train-rmse:284507.437500 test-rmse:162720.296875 [7] train-rmse:271000.968750 test-rmse:161336.437500 [8] train-rmse:261166.328125 test-rmse:160874.218750 [9] train-rmse:257725.906250 test-rmse:160522.078125 [10] train-rmse:253438.812500 test-rmse:160478.265625 [11] train-rmse:251538.156250 test-rmse:160508.390625 [12] train-rmse:250394.796875 test-rmse:160512.796875 [13] train-rmse:248676.671875 test-rmse:160506.453125 Stopping. Best iteration: [10] train-rmse:253438.812500 test-rmse:160478.265625 [1] train-rmse:619731.875000 test-rmse:275048.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:490640.625000 test-rmse:265093.375000 [3] train-rmse:411145.750000 test-rmse:275787.687500 [4] train-rmse:349715.375000 test-rmse:243645.828125 [5] train-rmse:317462.187500 test-rmse:220119.750000 [6] train-rmse:290316.656250 test-rmse:202814.515625 [7] train-rmse:273398.937500 test-rmse:190047.593750 [8] train-rmse:265169.437500 test-rmse:180616.921875 [9] train-rmse:258716.703125 test-rmse:185549.328125 [10] train-rmse:255449.515625 test-rmse:177828.562500 [11] train-rmse:252194.921875 test-rmse:171995.484375 [12] train-rmse:249453.187500 test-rmse:167539.265625 [13] train-rmse:247785.640625 test-rmse:164244.921875 [14] train-rmse:246574.109375 test-rmse:161719.828125 [15] train-rmse:246024.968750 test-rmse:159800.890625 [16] train-rmse:245376.187500 test-rmse:160017.546875 [17] train-rmse:244899.937500 test-rmse:158505.546875 [18] train-rmse:244499.828125 test-rmse:158173.406250 [19] train-rmse:244289.609375 test-rmse:158167.765625 [20] train-rmse:244025.343750 test-rmse:158122.437500 [1] train-rmse:549699.812500 test-rmse:6028738.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:428094.812500 test-rmse:5972225.500000 [3] train-rmse:350341.437500 test-rmse:5928560.000000 [4] train-rmse:290375.531250 test-rmse:5883637.000000 [5] train-rmse:252463.593750 test-rmse:5851600.000000 [6] train-rmse:226881.937500 test-rmse:5827025.000000 [7] train-rmse:210138.359375 test-rmse:5802347.500000 [8] train-rmse:200072.968750 test-rmse:5795194.500000 [9] train-rmse:194552.906250 test-rmse:5783622.000000 [10] train-rmse:191011.593750 test-rmse:5772099.000000 [11] train-rmse:189282.656250 test-rmse:5764289.000000 [12] train-rmse:187546.937500 test-rmse:5757395.500000 [13] train-rmse:186430.265625 test-rmse:5751807.000000 [14] train-rmse:185779.625000 test-rmse:5747327.500000 [15] train-rmse:185148.140625 test-rmse:5743254.500000 [16] train-rmse:184752.765625 test-rmse:5740158.000000 [17] train-rmse:184345.812500 test-rmse:5737340.000000 [18] train-rmse:184069.375000 test-rmse:5735232.000000 [19] train-rmse:183813.687500 test-rmse:5733604.000000 [20] train-rmse:183554.937500 test-rmse:5732302.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:417306.156250 test-rmse:1470914.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:345114.375000 test-rmse:1375391.625000 [3] train-rmse:294925.500000 test-rmse:1314999.125000 [4] train-rmse:260849.921875 test-rmse:1277755.875000 [5] train-rmse:238232.093750 test-rmse:1255512.750000 [6] train-rmse:223489.656250 test-rmse:1242754.000000 [7] train-rmse:214025.937500 test-rmse:1235696.000000 [8] train-rmse:207541.921875 test-rmse:1230025.875000 [9] train-rmse:203382.468750 test-rmse:1227234.375000 [10] train-rmse:200735.109375 test-rmse:1224418.375000 [11] train-rmse:199020.906250 test-rmse:1223185.625000 [12] train-rmse:197825.953125 test-rmse:1225055.625000 [13] train-rmse:197098.015625 test-rmse:1224976.875000 [14] train-rmse:196617.375000 test-rmse:1225258.625000 Stopping. Best iteration: [11] train-rmse:199020.906250 test-rmse:1223185.625000 [1] train-rmse:467811.593750 test-rmse:481281.593750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:395656.562500 test-rmse:426637.625000 [3] train-rmse:349177.218750 test-rmse:394841.281250 [4] train-rmse:304901.031250 test-rmse:373398.687500 [5] train-rmse:273808.562500 test-rmse:363036.000000 [6] train-rmse:252285.406250 test-rmse:359456.812500 [7] train-rmse:237388.171875 test-rmse:359866.781250 [8] train-rmse:227315.140625 test-rmse:361531.687500 [9] train-rmse:220541.062500 test-rmse:363930.031250 Stopping. Best iteration: [6] train-rmse:252285.406250 test-rmse:359456.812500 [1] train-rmse:492732.187500 test-rmse:101784.679688 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:412446.656250 test-rmse:76386.539062 [3] train-rmse:360403.062500 test-rmse:59943.617188 [4] train-rmse:311737.250000 test-rmse:49723.640625 [5] train-rmse:277898.187500 test-rmse:43783.441406 [6] train-rmse:254420.578125 test-rmse:40630.433594 [7] train-rmse:238590.562500 test-rmse:38938.394531 [8] train-rmse:228133.078125 test-rmse:38046.871094 [9] train-rmse:221242.781250 test-rmse:37579.363281 [10] train-rmse:216601.437500 test-rmse:37320.625000 [11] train-rmse:213359.343750 test-rmse:37192.964844 [12] train-rmse:211283.093750 test-rmse:37116.414062 [13] train-rmse:209849.828125 test-rmse:37114.089844 [14] train-rmse:208867.390625 test-rmse:37002.066406 [15] train-rmse:208234.000000 test-rmse:37027.035156 [16] train-rmse:207777.468750 test-rmse:37038.226562 [17] train-rmse:207466.375000 test-rmse:37046.746094 Stopping. Best iteration: [14] train-rmse:208867.390625 test-rmse:37002.066406 [1] train-rmse:484235.843750 test-rmse:196457.062500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:403560.218750 test-rmse:166885.296875 [3] train-rmse:351078.843750 test-rmse:162755.125000 [4] train-rmse:302589.781250 test-rmse:169760.359375 [5] train-rmse:268437.093750 test-rmse:179344.390625 [6] train-rmse:244957.531250 test-rmse:188054.296875 Stopping. Best iteration: [3] train-rmse:351078.843750 test-rmse:162755.125000 [1] train-rmse:326130.312500 test-rmse:2291018.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:252615.875000 test-rmse:2278091.250000 [3] train-rmse:198367.093750 test-rmse:2269898.500000 [4] train-rmse:158870.343750 test-rmse:2263911.500000 [5] train-rmse:130216.500000 test-rmse:2260558.750000 [6] train-rmse:110027.000000 test-rmse:2258341.750000 [7] train-rmse:96156.546875 test-rmse:2256873.000000 [8] train-rmse:86887.789062 test-rmse:2255683.750000 [9] train-rmse:80870.703125 test-rmse:2255071.250000 [10] train-rmse:77028.382812 test-rmse:2254720.250000 [11] train-rmse:74619.125000 test-rmse:2254477.750000 [12] train-rmse:73110.765625 test-rmse:2254464.000000 [13] train-rmse:72187.468750 test-rmse:2254490.750000 [14] train-rmse:71612.085938 test-rmse:2254488.750000 [15] train-rmse:71252.554688 test-rmse:2254546.750000 Stopping. Best iteration: [12] train-rmse:73110.765625 test-rmse:2254464.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:1460116.000000 test-rmse:3528966.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1213690.375000 test-rmse:3417065.000000 [3] train-rmse:1055972.500000 test-rmse:3360537.000000 [4] train-rmse:961999.750000 test-rmse:3301551.750000 [5] train-rmse:907773.000000 test-rmse:3266860.750000 [6] train-rmse:863423.937500 test-rmse:3257239.500000 [7] train-rmse:834608.562500 test-rmse:3260680.250000 [8] train-rmse:806076.250000 test-rmse:3261409.000000 [9] train-rmse:794679.625000 test-rmse:3276204.000000 Stopping. Best iteration: [6] train-rmse:863423.937500 test-rmse:3257239.500000 [1] train-rmse:1419147.875000 test-rmse:3393925.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1195335.875000 test-rmse:3240275.250000 [3] train-rmse:1060683.250000 test-rmse:3147177.250000 [4] train-rmse:961715.437500 test-rmse:3055545.750000 [5] train-rmse:899449.125000 test-rmse:2981568.750000 [6] train-rmse:856334.062500 test-rmse:2945686.500000 [7] train-rmse:827330.562500 test-rmse:2913964.250000 [8] train-rmse:800045.062500 test-rmse:2891633.750000 [9] train-rmse:783124.062500 test-rmse:2877148.250000 [10] train-rmse:761322.500000 test-rmse:2877985.250000 [11] train-rmse:749869.375000 test-rmse:2871909.750000 [12] train-rmse:742315.312500 test-rmse:2875137.000000 [13] train-rmse:735681.500000 test-rmse:2874389.750000 [14] train-rmse:732672.812500 test-rmse:2866459.250000 [15] train-rmse:730452.812500 test-rmse:2862514.000000 [16] train-rmse:722350.562500 test-rmse:2871595.500000 [17] train-rmse:716383.562500 test-rmse:2871294.000000 [18] train-rmse:711243.875000 test-rmse:2868094.500000 Stopping. Best iteration: [15] train-rmse:730452.812500 test-rmse:2862514.000000 [1] train-rmse:1468605.125000 test-rmse:12522629.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1229101.500000 test-rmse:12434431.000000 [3] train-rmse:1084032.625000 test-rmse:12389898.000000 [4] train-rmse:993392.500000 test-rmse:12327884.000000 [5] train-rmse:935889.562500 test-rmse:12279379.000000 [6] train-rmse:899482.625000 test-rmse:12264329.000000 [7] train-rmse:878911.375000 test-rmse:12246721.000000 [8] train-rmse:863228.812500 test-rmse:12222629.000000 [9] train-rmse:848195.000000 test-rmse:12206690.000000 [10] train-rmse:840107.187500 test-rmse:12193429.000000 [11] train-rmse:835983.875000 test-rmse:12186507.000000 [12] train-rmse:831936.375000 test-rmse:12178635.000000 [13] train-rmse:828395.562500 test-rmse:12176565.000000 [14] train-rmse:824504.250000 test-rmse:12172070.000000 [15] train-rmse:821678.625000 test-rmse:12172425.000000 [16] train-rmse:812179.937500 test-rmse:12172021.000000 [17] train-rmse:806081.375000 test-rmse:12171447.000000 [18] train-rmse:797721.437500 test-rmse:12172861.000000 [19] train-rmse:791687.937500 test-rmse:12172090.000000 [20] train-rmse:786299.500000 test-rmse:12173851.000000 Stopping. Best iteration: [17] train-rmse:806081.375000 test-rmse:12171447.000000 [1] train-rmse:1375249.500000 test-rmse:4833647.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1157119.375000 test-rmse:4593205.000000 [3] train-rmse:1004137.875000 test-rmse:4437115.000000 [4] train-rmse:907005.000000 test-rmse:4314294.000000 [5] train-rmse:843634.250000 test-rmse:4250186.500000 [6] train-rmse:803481.062500 test-rmse:4195054.500000 [7] train-rmse:776339.875000 test-rmse:4154341.000000 [8] train-rmse:755213.562500 test-rmse:4136027.750000 [9] train-rmse:736739.312500 test-rmse:4114019.000000 [10] train-rmse:722987.062500 test-rmse:4105477.750000 [11] train-rmse:711906.125000 test-rmse:4090473.250000 [12] train-rmse:704857.750000 test-rmse:4082833.750000 [13] train-rmse:694855.250000 test-rmse:4078389.000000 [14] train-rmse:689762.812500 test-rmse:4067337.000000 [15] train-rmse:684824.062500 test-rmse:4066702.500000 [16] train-rmse:678322.625000 test-rmse:4066465.000000 [17] train-rmse:676148.750000 test-rmse:4059685.500000 [18] train-rmse:670825.000000 test-rmse:4060294.750000 [19] train-rmse:667526.250000 test-rmse:4060411.250000 [20] train-rmse:662058.750000 test-rmse:4058206.250000 [1] train-rmse:1523092.000000 test-rmse:10448545.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1269251.875000 test-rmse:10379317.000000 [3] train-rmse:1110964.250000 test-rmse:10314662.000000 [4] train-rmse:1015384.562500 test-rmse:10295700.000000 [5] train-rmse:958921.500000 test-rmse:10274435.000000 [6] train-rmse:915353.187500 test-rmse:10281316.000000 [7] train-rmse:884180.750000 test-rmse:10297077.000000 [8] train-rmse:857055.937500 test-rmse:10303329.000000 Stopping. Best iteration: [5] train-rmse:958921.500000 test-rmse:10274435.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:448936.593750 test-rmse:240689.703125 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:357120.843750 test-rmse:172092.125000 [3] train-rmse:299187.156250 test-rmse:136528.125000 [4] train-rmse:264011.250000 test-rmse:125563.531250 [5] train-rmse:243621.406250 test-rmse:124583.695312 [6] train-rmse:231912.703125 test-rmse:127355.656250 [7] train-rmse:225223.203125 test-rmse:132202.703125 [8] train-rmse:221541.484375 test-rmse:136006.968750 Stopping. Best iteration: [5] train-rmse:243621.406250 test-rmse:124583.695312 [1] train-rmse:379741.187500 test-rmse:693703.562500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:305007.218750 test-rmse:612158.437500 [3] train-rmse:258210.531250 test-rmse:552880.937500 [4] train-rmse:230305.484375 test-rmse:513922.968750 [5] train-rmse:214208.625000 test-rmse:488379.937500 [6] train-rmse:204945.562500 test-rmse:477226.468750 [7] train-rmse:199553.265625 test-rmse:471787.343750 [8] train-rmse:196488.187500 test-rmse:467792.656250 [9] train-rmse:194719.828125 test-rmse:464050.906250 [10] train-rmse:193633.500000 test-rmse:458538.500000 [11] train-rmse:192885.046875 test-rmse:457130.625000 [12] train-rmse:192538.843750 test-rmse:454536.156250 [13] train-rmse:192122.625000 test-rmse:454980.343750 [14] train-rmse:191873.921875 test-rmse:453878.156250 [15] train-rmse:191743.718750 test-rmse:452289.875000 [16] train-rmse:191487.281250 test-rmse:450938.062500 [17] train-rmse:191361.406250 test-rmse:450136.250000 [18] train-rmse:191281.062500 test-rmse:450075.187500 [19] train-rmse:191202.609375 test-rmse:449380.718750 [20] train-rmse:191158.968750 test-rmse:448864.875000 [1] train-rmse:413765.968750 test-rmse:940952.187500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:324150.625000 test-rmse:909926.875000 [3] train-rmse:266180.218750 test-rmse:891726.125000 [4] train-rmse:230515.968750 test-rmse:882042.625000 [5] train-rmse:209434.000000 test-rmse:876497.750000 [6] train-rmse:197230.203125 test-rmse:873258.562500 [7] train-rmse:190558.703125 test-rmse:871605.000000 [8] train-rmse:186580.468750 test-rmse:870328.500000 [9] train-rmse:184585.453125 test-rmse:869753.187500 [10] train-rmse:183161.875000 test-rmse:869318.875000 [11] train-rmse:182455.921875 test-rmse:869056.250000 [12] train-rmse:182073.156250 test-rmse:868790.000000 [13] train-rmse:181680.265625 test-rmse:868911.687500 [14] train-rmse:181546.765625 test-rmse:869100.125000 [15] train-rmse:181445.890625 test-rmse:869109.937500 Stopping. Best iteration: [12] train-rmse:182073.156250 test-rmse:868790.000000 [1] train-rmse:401680.406250 test-rmse:479883.031250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:318685.156250 test-rmse:382131.312500 [3] train-rmse:265870.625000 test-rmse:324268.156250 [4] train-rmse:233394.000000 test-rmse:300440.406250 [5] train-rmse:214199.656250 test-rmse:292152.250000 [6] train-rmse:202954.796875 test-rmse:290547.875000 [7] train-rmse:196427.859375 test-rmse:292050.343750 [8] train-rmse:192482.937500 test-rmse:295205.218750 [9] train-rmse:190241.203125 test-rmse:298536.625000 Stopping. Best iteration: [6] train-rmse:202954.796875 test-rmse:290547.875000 [1] train-rmse:445970.531250 test-rmse:282818.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:356107.843750 test-rmse:213540.250000 [3] train-rmse:299779.468750 test-rmse:168982.078125 [4] train-rmse:265616.125000 test-rmse:141689.421875 [5] train-rmse:245926.500000 test-rmse:125528.007812 [6] train-rmse:234616.468750 test-rmse:118359.640625 [7] train-rmse:228155.171875 test-rmse:114217.859375 [8] train-rmse:224411.031250 test-rmse:112535.664062 [9] train-rmse:222218.687500 test-rmse:111835.960938 [10] train-rmse:221018.750000 test-rmse:111578.054688 [11] train-rmse:220324.250000 test-rmse:111517.226562 [12] train-rmse:219645.046875 test-rmse:111412.171875 [13] train-rmse:219199.218750 test-rmse:111384.500000 [14] train-rmse:218899.296875 test-rmse:111266.679688 [15] train-rmse:218696.546875 test-rmse:111297.414062 [16] train-rmse:218545.687500 test-rmse:111236.734375 [17] train-rmse:218451.046875 test-rmse:111250.953125 [18] train-rmse:218362.093750 test-rmse:111248.406250 [19] train-rmse:218274.562500 test-rmse:111203.585938 [20] train-rmse:218218.875000 test-rmse:111245.429688
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:381680.093750 test-rmse:366662.468750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:295880.406250 test-rmse:286085.906250 [3] train-rmse:242027.203125 test-rmse:233891.171875 [4] train-rmse:210054.578125 test-rmse:203334.468750 [5] train-rmse:191574.156250 test-rmse:187284.453125 [6] train-rmse:181820.968750 test-rmse:179297.718750 [7] train-rmse:176319.359375 test-rmse:175873.390625 [8] train-rmse:173681.250000 test-rmse:174808.796875 [9] train-rmse:172073.375000 test-rmse:174076.093750 [10] train-rmse:171260.078125 test-rmse:174209.062500 [11] train-rmse:170659.656250 test-rmse:174332.234375 [12] train-rmse:170374.937500 test-rmse:174238.515625 Stopping. Best iteration: [9] train-rmse:172073.375000 test-rmse:174076.093750 [1] train-rmse:373174.062500 test-rmse:393011.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:286653.968750 test-rmse:312340.406250 [3] train-rmse:232014.921875 test-rmse:267646.468750 [4] train-rmse:199302.390625 test-rmse:246882.562500 [5] train-rmse:180794.531250 test-rmse:238633.453125 [6] train-rmse:170610.609375 test-rmse:237793.656250 [7] train-rmse:165281.546875 test-rmse:239613.343750 [8] train-rmse:162512.578125 test-rmse:242184.546875 [9] train-rmse:161047.421875 test-rmse:244825.984375 Stopping. Best iteration: [6] train-rmse:170610.609375 test-rmse:237793.656250 [1] train-rmse:370419.250000 test-rmse:424952.468750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:288229.593750 test-rmse:336678.281250 [3] train-rmse:237255.000000 test-rmse:283332.218750 [4] train-rmse:206767.593750 test-rmse:249280.906250 [5] train-rmse:189970.687500 test-rmse:229142.968750 [6] train-rmse:180917.328125 test-rmse:217301.578125 [7] train-rmse:176094.968750 test-rmse:210125.859375 [8] train-rmse:173428.421875 test-rmse:204850.171875 [9] train-rmse:172102.421875 test-rmse:201588.171875 [10] train-rmse:171267.109375 test-rmse:199717.234375 [11] train-rmse:170787.921875 test-rmse:198096.250000 [12] train-rmse:170522.265625 test-rmse:197266.656250 [13] train-rmse:170317.343750 test-rmse:196858.593750 [14] train-rmse:170224.718750 test-rmse:196554.593750 [15] train-rmse:170112.453125 test-rmse:196816.000000 [16] train-rmse:170003.015625 test-rmse:196975.218750 [17] train-rmse:169898.421875 test-rmse:197209.609375 Stopping. Best iteration: [14] train-rmse:170224.718750 test-rmse:196554.593750 [1] train-rmse:383190.031250 test-rmse:347271.968750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:297692.937500 test-rmse:256651.656250 [3] train-rmse:244079.843750 test-rmse:204854.671875 [4] train-rmse:212265.437500 test-rmse:179332.390625 [5] train-rmse:194027.031250 test-rmse:165827.328125 [6] train-rmse:184104.562500 test-rmse:160272.781250 [7] train-rmse:178880.109375 test-rmse:159807.187500 [8] train-rmse:175962.375000 test-rmse:160508.546875 [9] train-rmse:174535.406250 test-rmse:161680.218750 [10] train-rmse:172898.062500 test-rmse:163270.171875 Stopping. Best iteration: [7] train-rmse:178880.109375 test-rmse:159807.187500 [1] train-rmse:385546.468750 test-rmse:339470.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:299045.781250 test-rmse:260301.000000 [3] train-rmse:244938.312500 test-rmse:209969.390625 [4] train-rmse:212715.578125 test-rmse:182690.546875 [5] train-rmse:194783.359375 test-rmse:169436.171875 [6] train-rmse:185065.328125 test-rmse:165918.109375 [7] train-rmse:179086.718750 test-rmse:164774.093750 [8] train-rmse:176160.750000 test-rmse:165589.203125 [9] train-rmse:174649.250000 test-rmse:168273.375000 [10] train-rmse:173729.406250 test-rmse:168754.031250 Stopping. Best iteration: [7] train-rmse:179086.718750 test-rmse:164774.093750
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:1271614.250000 test-rmse:1628932.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1044864.687500 test-rmse:1407846.625000 [3] train-rmse:904576.750000 test-rmse:1283450.250000 [4] train-rmse:815669.562500 test-rmse:1199198.625000 [5] train-rmse:750912.375000 test-rmse:1162703.625000 [6] train-rmse:712459.062500 test-rmse:1142746.000000 [7] train-rmse:684959.250000 test-rmse:1115634.000000 [8] train-rmse:664855.687500 test-rmse:1103450.250000 [9] train-rmse:650966.750000 test-rmse:1093186.375000 [10] train-rmse:637443.250000 test-rmse:1088114.750000 [11] train-rmse:628036.000000 test-rmse:1086207.500000 [12] train-rmse:619076.750000 test-rmse:1081002.625000 [13] train-rmse:612177.625000 test-rmse:1080457.500000 [14] train-rmse:607999.437500 test-rmse:1080400.625000 [15] train-rmse:601857.062500 test-rmse:1079393.250000 [16] train-rmse:598752.437500 test-rmse:1077728.500000 [17] train-rmse:595083.750000 test-rmse:1080675.375000 [18] train-rmse:593803.187500 test-rmse:1080962.875000 [19] train-rmse:590614.437500 test-rmse:1086147.750000 Stopping. Best iteration: [16] train-rmse:598752.437500 test-rmse:1077728.500000 [1] train-rmse:1262181.625000 test-rmse:1916986.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1031527.437500 test-rmse:1741391.750000 [3] train-rmse:884410.187500 test-rmse:1630473.250000 [4] train-rmse:792180.500000 test-rmse:1555653.875000 [5] train-rmse:737989.125000 test-rmse:1511763.000000 [6] train-rmse:694606.187500 test-rmse:1479554.375000 [7] train-rmse:671704.187500 test-rmse:1457165.500000 [8] train-rmse:651553.562500 test-rmse:1444710.375000 [9] train-rmse:642440.375000 test-rmse:1433969.875000 [10] train-rmse:634251.125000 test-rmse:1427086.000000 [11] train-rmse:626921.625000 test-rmse:1423028.875000 [12] train-rmse:619767.000000 test-rmse:1413816.125000 [13] train-rmse:616398.562500 test-rmse:1410934.625000 [14] train-rmse:609931.187500 test-rmse:1405128.375000 [15] train-rmse:607373.500000 test-rmse:1402270.875000 [16] train-rmse:604092.875000 test-rmse:1400111.250000 [17] train-rmse:599814.187500 test-rmse:1398143.000000 [18] train-rmse:597874.625000 test-rmse:1398893.000000 [19] train-rmse:595123.812500 test-rmse:1398855.125000 [20] train-rmse:592849.312500 test-rmse:1400914.000000 Stopping. Best iteration: [17] train-rmse:599814.187500 test-rmse:1398143.000000 [1] train-rmse:1320984.875000 test-rmse:1406315.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1079047.250000 test-rmse:1211317.875000 [3] train-rmse:935989.937500 test-rmse:1107248.250000 [4] train-rmse:845109.750000 test-rmse:1085884.375000 [5] train-rmse:787469.937500 test-rmse:1096291.500000 [6] train-rmse:756295.125000 test-rmse:1101326.875000 [7] train-rmse:730352.500000 test-rmse:1093425.500000 Stopping. Best iteration: [4] train-rmse:845109.750000 test-rmse:1085884.375000 [1] train-rmse:1259037.875000 test-rmse:1888910.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1026011.812500 test-rmse:1704403.125000 [3] train-rmse:886973.000000 test-rmse:1602919.375000 [4] train-rmse:799636.750000 test-rmse:1544025.500000 [5] train-rmse:743125.687500 test-rmse:1492200.500000 [6] train-rmse:712392.375000 test-rmse:1477205.750000 [7] train-rmse:680621.625000 test-rmse:1483890.625000 [8] train-rmse:665104.500000 test-rmse:1466548.875000 [9] train-rmse:652319.562500 test-rmse:1466926.250000 [10] train-rmse:642136.375000 test-rmse:1458643.125000 [11] train-rmse:633927.062500 test-rmse:1465345.250000 [12] train-rmse:630399.000000 test-rmse:1461472.750000 [13] train-rmse:627109.062500 test-rmse:1460165.000000 Stopping. Best iteration: [10] train-rmse:642136.375000 test-rmse:1458643.125000 [1] train-rmse:1281375.500000 test-rmse:3470479.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1047999.312500 test-rmse:3377211.750000 [3] train-rmse:908022.000000 test-rmse:3317579.000000 [4] train-rmse:814192.187500 test-rmse:3260880.000000 [5] train-rmse:752530.500000 test-rmse:3238564.000000 [6] train-rmse:711722.062500 test-rmse:3217691.750000 [7] train-rmse:690362.250000 test-rmse:3207588.500000 [8] train-rmse:670492.187500 test-rmse:3194121.250000 [9] train-rmse:655455.937500 test-rmse:3188492.500000 [10] train-rmse:648961.187500 test-rmse:3183284.000000 [11] train-rmse:641043.437500 test-rmse:3180071.750000 [12] train-rmse:635996.500000 test-rmse:3179070.250000 [13] train-rmse:626962.625000 test-rmse:3178167.250000 [14] train-rmse:620635.500000 test-rmse:3177067.500000 [15] train-rmse:615838.375000 test-rmse:3176952.250000 [16] train-rmse:609731.375000 test-rmse:3176262.750000 [17] train-rmse:604290.312500 test-rmse:3174663.000000 [18] train-rmse:602041.500000 test-rmse:3172956.750000 [19] train-rmse:598633.875000 test-rmse:3165683.500000 [20] train-rmse:597143.187500 test-rmse:3164472.750000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:718428.875000 test-rmse:1736563.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:567947.812500 test-rmse:1642969.250000 [3] train-rmse:475178.781250 test-rmse:1588877.875000 [4] train-rmse:407377.843750 test-rmse:1551620.250000 [5] train-rmse:363367.406250 test-rmse:1530331.000000 [6] train-rmse:338682.062500 test-rmse:1516801.375000 [7] train-rmse:321811.531250 test-rmse:1505938.375000 [8] train-rmse:311741.718750 test-rmse:1497502.000000 [9] train-rmse:305093.093750 test-rmse:1488887.625000 [10] train-rmse:300619.687500 test-rmse:1481072.125000 [11] train-rmse:298179.375000 test-rmse:1478182.125000 [12] train-rmse:296477.625000 test-rmse:1474805.125000 [13] train-rmse:295026.406250 test-rmse:1474227.875000 [14] train-rmse:294136.343750 test-rmse:1472754.375000 [15] train-rmse:293283.281250 test-rmse:1472189.375000 [16] train-rmse:292731.531250 test-rmse:1471751.125000 [17] train-rmse:292322.968750 test-rmse:1471300.250000 [18] train-rmse:291957.187500 test-rmse:1471131.375000 [19] train-rmse:291621.093750 test-rmse:1470405.500000 [20] train-rmse:291350.812500 test-rmse:1471005.125000 [1] train-rmse:651458.000000 test-rmse:3887087.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:519415.406250 test-rmse:3784430.750000 [3] train-rmse:438474.625000 test-rmse:3690031.000000 [4] train-rmse:384496.781250 test-rmse:3637295.750000 [5] train-rmse:347268.718750 test-rmse:3583548.750000 [6] train-rmse:325331.468750 test-rmse:3551414.250000 [7] train-rmse:308214.062500 test-rmse:3537014.000000 [8] train-rmse:299471.906250 test-rmse:3546043.250000 [9] train-rmse:293203.718750 test-rmse:3535220.500000 [10] train-rmse:289431.531250 test-rmse:3538641.250000 [11] train-rmse:284984.000000 test-rmse:3528866.500000 [12] train-rmse:282267.843750 test-rmse:3523592.750000 [13] train-rmse:280325.218750 test-rmse:3518820.000000 [14] train-rmse:279063.875000 test-rmse:3515380.000000 [15] train-rmse:278406.593750 test-rmse:3513507.000000 [16] train-rmse:277923.093750 test-rmse:3511727.500000 [17] train-rmse:277491.593750 test-rmse:3511282.750000 [18] train-rmse:277248.093750 test-rmse:3510523.500000 [19] train-rmse:276815.625000 test-rmse:3510460.750000 [20] train-rmse:276738.250000 test-rmse:3512796.000000 [1] train-rmse:692430.875000 test-rmse:3088479.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:541298.937500 test-rmse:2964883.000000 [3] train-rmse:447286.218750 test-rmse:2882126.250000 [4] train-rmse:389505.812500 test-rmse:2822947.000000 [5] train-rmse:357858.000000 test-rmse:2783879.000000 [6] train-rmse:329978.218750 test-rmse:2747051.500000 [7] train-rmse:310224.562500 test-rmse:2720097.750000 [8] train-rmse:298792.718750 test-rmse:2700634.000000 [9] train-rmse:292189.125000 test-rmse:2685318.500000 [10] train-rmse:287686.187500 test-rmse:2675135.250000 [11] train-rmse:284989.593750 test-rmse:2667153.750000 [12] train-rmse:282744.937500 test-rmse:2662653.000000 [13] train-rmse:281033.875000 test-rmse:2657427.750000 [14] train-rmse:280015.718750 test-rmse:2653134.500000 [15] train-rmse:279333.781250 test-rmse:2649798.250000 [16] train-rmse:278899.000000 test-rmse:2648232.750000 [17] train-rmse:278489.125000 test-rmse:2647157.750000 [18] train-rmse:278288.656250 test-rmse:2647111.250000 [19] train-rmse:278205.343750 test-rmse:2646387.500000 [20] train-rmse:278157.406250 test-rmse:2646337.750000 [1] train-rmse:755687.000000 test-rmse:1146811.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:598985.687500 test-rmse:1078824.625000 [3] train-rmse:492081.281250 test-rmse:1047130.375000 [4] train-rmse:433748.312500 test-rmse:1018118.187500 [5] train-rmse:392149.156250 test-rmse:1005840.500000 [6] train-rmse:367211.312500 test-rmse:991108.437500 [7] train-rmse:352938.187500 test-rmse:985400.437500 [8] train-rmse:345106.281250 test-rmse:991437.000000 [9] train-rmse:336324.343750 test-rmse:995966.875000 [10] train-rmse:331792.593750 test-rmse:994486.562500 Stopping. Best iteration: [7] train-rmse:352938.187500 test-rmse:985400.437500 [1] train-rmse:653994.312500 test-rmse:6116294.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:512978.812500 test-rmse:5981879.500000 [3] train-rmse:419124.625000 test-rmse:5922645.500000 [4] train-rmse:362513.312500 test-rmse:5882230.500000 [5] train-rmse:331559.375000 test-rmse:5836378.000000 [6] train-rmse:310241.187500 test-rmse:5818073.000000 [7] train-rmse:297045.687500 test-rmse:5794029.000000 [8] train-rmse:287988.625000 test-rmse:5788934.000000 [9] train-rmse:283134.156250 test-rmse:5785704.000000 [10] train-rmse:280137.218750 test-rmse:5783372.500000 [11] train-rmse:278033.031250 test-rmse:5787042.000000 [12] train-rmse:276884.906250 test-rmse:5789789.000000 [13] train-rmse:275618.562500 test-rmse:5789380.500000 Stopping. Best iteration: [10] train-rmse:280137.218750 test-rmse:5783372.500000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:1067879.250000 test-rmse:2091861.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:886958.625000 test-rmse:2025301.625000 [3] train-rmse:772438.000000 test-rmse:1992238.750000 [4] train-rmse:708576.062500 test-rmse:1973403.500000 [5] train-rmse:673674.812500 test-rmse:1963998.000000 [6] train-rmse:653769.687500 test-rmse:1957439.000000 [7] train-rmse:639141.875000 test-rmse:1954631.625000 [8] train-rmse:632526.687500 test-rmse:1952766.625000 [9] train-rmse:628811.875000 test-rmse:1951092.625000 [10] train-rmse:626646.312500 test-rmse:1950643.875000 [11] train-rmse:625585.375000 test-rmse:1950621.875000 [12] train-rmse:624921.250000 test-rmse:1950124.000000 [13] train-rmse:622362.125000 test-rmse:1950416.500000 [14] train-rmse:621491.562500 test-rmse:1949263.500000 [15] train-rmse:620849.125000 test-rmse:1949644.000000 [16] train-rmse:619791.187500 test-rmse:1950375.500000 [17] train-rmse:618955.437500 test-rmse:1949393.125000 Stopping. Best iteration: [14] train-rmse:621491.562500 test-rmse:1949263.500000 [1] train-rmse:948339.875000 test-rmse:1628460.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:784746.000000 test-rmse:1435849.250000 [3] train-rmse:680722.125000 test-rmse:1308359.000000 [4] train-rmse:618764.562500 test-rmse:1244336.750000 [5] train-rmse:582260.125000 test-rmse:1211991.500000 [6] train-rmse:563965.875000 test-rmse:1195039.125000 [7] train-rmse:550144.125000 test-rmse:1187340.000000 [8] train-rmse:544836.937500 test-rmse:1183372.125000 [9] train-rmse:541199.437500 test-rmse:1180568.875000 [10] train-rmse:536160.375000 test-rmse:1187528.250000 [11] train-rmse:533868.375000 test-rmse:1195043.625000 [12] train-rmse:532558.375000 test-rmse:1194199.250000 Stopping. Best iteration: [9] train-rmse:541199.437500 test-rmse:1180568.875000 [1] train-rmse:1016726.250000 test-rmse:6459909.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:832762.000000 test-rmse:6430120.500000 [3] train-rmse:723037.812500 test-rmse:6408700.000000 [4] train-rmse:661235.312500 test-rmse:6402452.000000 [5] train-rmse:627971.500000 test-rmse:6395733.000000 [6] train-rmse:609956.625000 test-rmse:6391175.500000 [7] train-rmse:598601.937500 test-rmse:6388657.000000 [8] train-rmse:593119.875000 test-rmse:6387166.000000 [9] train-rmse:589631.375000 test-rmse:6387057.500000 [10] train-rmse:587080.375000 test-rmse:6385964.500000 [11] train-rmse:585201.250000 test-rmse:6385836.500000 [12] train-rmse:584489.937500 test-rmse:6385941.000000 [13] train-rmse:582488.875000 test-rmse:6385244.500000 [14] train-rmse:581592.562500 test-rmse:6385149.000000 [15] train-rmse:580464.562500 test-rmse:6382975.500000 [16] train-rmse:579998.812500 test-rmse:6381192.000000 [17] train-rmse:579249.062500 test-rmse:6380929.000000 [18] train-rmse:579039.687500 test-rmse:6379562.500000 [19] train-rmse:578020.500000 test-rmse:6378809.000000 [20] train-rmse:577119.000000 test-rmse:6378654.500000 [1] train-rmse:1066071.875000 test-rmse:959691.937500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:881868.625000 test-rmse:837059.437500 [3] train-rmse:770590.687500 test-rmse:766260.312500 [4] train-rmse:706697.750000 test-rmse:722298.562500 [5] train-rmse:672230.187500 test-rmse:685975.875000 [6] train-rmse:649652.500000 test-rmse:671462.625000 [7] train-rmse:636195.750000 test-rmse:660492.437500 [8] train-rmse:630056.062500 test-rmse:657955.750000 [9] train-rmse:626485.062500 test-rmse:653310.562500 [10] train-rmse:623433.875000 test-rmse:638495.437500 [11] train-rmse:622193.187500 test-rmse:637242.375000 [12] train-rmse:620684.937500 test-rmse:623744.437500 [13] train-rmse:619537.875000 test-rmse:624202.812500 [14] train-rmse:617601.937500 test-rmse:617456.875000 [15] train-rmse:616632.375000 test-rmse:613323.062500 [16] train-rmse:615874.687500 test-rmse:613332.500000 [17] train-rmse:615457.062500 test-rmse:614501.187500 [18] train-rmse:614897.500000 test-rmse:614404.062500 Stopping. Best iteration: [15] train-rmse:616632.375000 test-rmse:613323.062500 [1] train-rmse:1056450.000000 test-rmse:1038395.562500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:872357.687500 test-rmse:912433.875000 [3] train-rmse:764671.937500 test-rmse:826368.062500 [4] train-rmse:700282.062500 test-rmse:771749.000000 [5] train-rmse:664761.812500 test-rmse:744523.375000 [6] train-rmse:644674.750000 test-rmse:712959.312500 [7] train-rmse:633946.250000 test-rmse:700291.000000 [8] train-rmse:625387.875000 test-rmse:693585.562500 [9] train-rmse:619562.125000 test-rmse:689185.875000 [10] train-rmse:615897.187500 test-rmse:701109.562500 [11] train-rmse:614080.500000 test-rmse:708737.750000 [12] train-rmse:611612.375000 test-rmse:709911.312500 Stopping. Best iteration: [9] train-rmse:619562.125000 test-rmse:689185.875000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:1880778.250000 test-rmse:4268543.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1584871.375000 test-rmse:4076400.250000 [3] train-rmse:1400639.000000 test-rmse:3954088.500000 [4] train-rmse:1291714.000000 test-rmse:3874327.750000 [5] train-rmse:1214231.375000 test-rmse:3829593.500000 [6] train-rmse:1169705.500000 test-rmse:3797179.000000 [7] train-rmse:1122137.250000 test-rmse:3772779.250000 [8] train-rmse:1092745.875000 test-rmse:3753460.250000 [9] train-rmse:1071467.750000 test-rmse:3730625.000000 [10] train-rmse:1046728.625000 test-rmse:3721467.000000 [11] train-rmse:1033603.687500 test-rmse:3717453.750000 [12] train-rmse:1019402.875000 test-rmse:3712839.000000 [13] train-rmse:1010405.000000 test-rmse:3715387.500000 [14] train-rmse:1004769.625000 test-rmse:3710700.750000 [15] train-rmse:991100.625000 test-rmse:3710789.750000 [16] train-rmse:987195.687500 test-rmse:3709889.750000 [17] train-rmse:977836.750000 test-rmse:3698180.250000 [18] train-rmse:973339.562500 test-rmse:3694425.250000 [19] train-rmse:966298.687500 test-rmse:3688962.000000 [20] train-rmse:958718.500000 test-rmse:3690629.000000 [1] train-rmse:1892723.000000 test-rmse:7187174.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1590945.625000 test-rmse:7007940.500000 [3] train-rmse:1415663.500000 test-rmse:6903344.000000 [4] train-rmse:1306466.000000 test-rmse:6859273.500000 [5] train-rmse:1230778.500000 test-rmse:6826468.500000 [6] train-rmse:1181962.875000 test-rmse:6807003.500000 [7] train-rmse:1151800.625000 test-rmse:6787062.000000 [8] train-rmse:1119833.375000 test-rmse:6766374.000000 [9] train-rmse:1103108.000000 test-rmse:6772339.500000 [10] train-rmse:1076830.500000 test-rmse:6759849.500000 [11] train-rmse:1055464.250000 test-rmse:6753869.500000 [12] train-rmse:1047687.125000 test-rmse:6748578.500000 [13] train-rmse:1040096.625000 test-rmse:6743991.000000 [14] train-rmse:1032209.875000 test-rmse:6741625.000000 [15] train-rmse:1022279.500000 test-rmse:6738737.000000 [16] train-rmse:1014025.625000 test-rmse:6735465.500000 [17] train-rmse:1006376.125000 test-rmse:6736586.000000 [18] train-rmse:988383.500000 test-rmse:6726131.500000 [19] train-rmse:978380.000000 test-rmse:6726998.000000 [20] train-rmse:975929.562500 test-rmse:6727042.500000 [1] train-rmse:1959096.750000 test-rmse:29099722.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1633766.375000 test-rmse:28997462.000000 [3] train-rmse:1438540.750000 test-rmse:28913472.000000 [4] train-rmse:1319119.875000 test-rmse:28819226.000000 [5] train-rmse:1232862.375000 test-rmse:28759670.000000 [6] train-rmse:1183456.500000 test-rmse:28712310.000000 [7] train-rmse:1151831.625000 test-rmse:28682036.000000 [8] train-rmse:1131863.250000 test-rmse:28641726.000000 [9] train-rmse:1114076.250000 test-rmse:28632288.000000 [10] train-rmse:1089745.125000 test-rmse:28619634.000000 [11] train-rmse:1081458.000000 test-rmse:28601120.000000 [12] train-rmse:1063630.625000 test-rmse:28591808.000000 [13] train-rmse:1056223.250000 test-rmse:28576226.000000 [14] train-rmse:1050375.500000 test-rmse:28575686.000000 [15] train-rmse:1046000.000000 test-rmse:28588326.000000 [16] train-rmse:1037557.312500 test-rmse:28589062.000000 [17] train-rmse:1031839.687500 test-rmse:28591452.000000 Stopping. Best iteration: [14] train-rmse:1050375.500000 test-rmse:28575686.000000 [1] train-rmse:1899065.875000 test-rmse:6077079.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1599019.000000 test-rmse:5921071.500000 [3] train-rmse:1414382.625000 test-rmse:5824422.500000 [4] train-rmse:1284470.500000 test-rmse:5759804.000000 [5] train-rmse:1200078.250000 test-rmse:5711442.000000 [6] train-rmse:1144598.000000 test-rmse:5671786.000000 [7] train-rmse:1115379.000000 test-rmse:5646145.000000 [8] train-rmse:1088104.625000 test-rmse:5625868.500000 [9] train-rmse:1072080.125000 test-rmse:5613318.000000 [10] train-rmse:1057460.000000 test-rmse:5603164.500000 [11] train-rmse:1050683.500000 test-rmse:5595777.500000 [12] train-rmse:1036390.812500 test-rmse:5590377.500000 [13] train-rmse:1028706.875000 test-rmse:5582571.500000 [14] train-rmse:1023886.562500 test-rmse:5582209.000000 [15] train-rmse:1018445.062500 test-rmse:5581752.000000 [16] train-rmse:1009807.937500 test-rmse:5582329.500000 [17] train-rmse:1001559.312500 test-rmse:5578672.500000 [18] train-rmse:999771.562500 test-rmse:5579725.500000 [19] train-rmse:990983.062500 test-rmse:5578428.000000 [20] train-rmse:984855.937500 test-rmse:5568880.000000 [1] train-rmse:1773962.125000 test-rmse:14293887.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:1479658.375000 test-rmse:14145563.000000 [3] train-rmse:1302264.500000 test-rmse:14041594.000000 [4] train-rmse:1201498.375000 test-rmse:13954613.000000 [5] train-rmse:1141709.875000 test-rmse:13896102.000000 [6] train-rmse:1101319.250000 test-rmse:13864942.000000 [7] train-rmse:1067945.000000 test-rmse:13840294.000000 [8] train-rmse:1037047.000000 test-rmse:13817550.000000 [9] train-rmse:1017682.937500 test-rmse:13791907.000000 [10] train-rmse:1001046.750000 test-rmse:13783430.000000 [11] train-rmse:978135.687500 test-rmse:13773266.000000 [12] train-rmse:965867.687500 test-rmse:13765878.000000 [13] train-rmse:949487.937500 test-rmse:13761877.000000 [14] train-rmse:941327.812500 test-rmse:13756958.000000 [15] train-rmse:926353.187500 test-rmse:13746449.000000 [16] train-rmse:915912.812500 test-rmse:13734217.000000 [17] train-rmse:905587.875000 test-rmse:13722438.000000 [18] train-rmse:901952.500000 test-rmse:13721684.000000 [19] train-rmse:896516.437500 test-rmse:13718687.000000 [20] train-rmse:894843.437500 test-rmse:13717123.000000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:896222.812500 test-rmse:1297302.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:731208.375000 test-rmse:1124746.625000 [3] train-rmse:627431.250000 test-rmse:1012275.312500 [4] train-rmse:564164.312500 test-rmse:937123.187500 [5] train-rmse:526190.062500 test-rmse:887330.687500 [6] train-rmse:502923.687500 test-rmse:842495.750000 [7] train-rmse:485651.750000 test-rmse:806548.937500 [8] train-rmse:477164.593750 test-rmse:787537.625000 [9] train-rmse:472202.937500 test-rmse:777894.125000 [10] train-rmse:468192.437500 test-rmse:767450.187500 [11] train-rmse:466491.437500 test-rmse:762668.750000 [12] train-rmse:465307.281250 test-rmse:756523.250000 [13] train-rmse:464495.468750 test-rmse:751058.562500 [14] train-rmse:460332.375000 test-rmse:743872.625000 [15] train-rmse:459784.968750 test-rmse:743140.375000 [16] train-rmse:458427.781250 test-rmse:743729.375000 [17] train-rmse:457931.718750 test-rmse:741563.500000 [18] train-rmse:456872.000000 test-rmse:741713.500000 [19] train-rmse:456679.343750 test-rmse:741524.125000 [20] train-rmse:456583.312500 test-rmse:741696.687500 [1] train-rmse:948756.562500 test-rmse:841365.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:765182.312500 test-rmse:691866.562500 [3] train-rmse:649587.062500 test-rmse:636518.562500 [4] train-rmse:584763.062500 test-rmse:625622.437500 [5] train-rmse:535615.500000 test-rmse:619606.062500 [6] train-rmse:507402.937500 test-rmse:616433.375000 [7] train-rmse:490542.312500 test-rmse:631745.875000 [8] train-rmse:479934.843750 test-rmse:638351.375000 [9] train-rmse:471960.875000 test-rmse:639042.812500 Stopping. Best iteration: [6] train-rmse:507402.937500 test-rmse:616433.375000 [1] train-rmse:975930.125000 test-rmse:622498.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:792763.437500 test-rmse:486106.312500 [3] train-rmse:681497.625000 test-rmse:428364.750000 [4] train-rmse:602805.187500 test-rmse:409484.437500 [5] train-rmse:562069.750000 test-rmse:414195.843750 [6] train-rmse:532838.812500 test-rmse:436473.718750 [7] train-rmse:517899.375000 test-rmse:452221.062500 Stopping. Best iteration: [4] train-rmse:602805.187500 test-rmse:409484.437500 [1] train-rmse:949764.500000 test-rmse:876972.312500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:773161.437500 test-rmse:752400.937500 [3] train-rmse:665027.000000 test-rmse:672625.812500 [4] train-rmse:598602.312500 test-rmse:621992.187500 [5] train-rmse:558931.687500 test-rmse:590935.562500 [6] train-rmse:536780.187500 test-rmse:571600.437500 [7] train-rmse:511710.593750 test-rmse:563178.562500 [8] train-rmse:501046.718750 test-rmse:559852.062500 [9] train-rmse:495406.937500 test-rmse:535902.500000 [10] train-rmse:491467.750000 test-rmse:535966.437500 [11] train-rmse:481938.687500 test-rmse:535893.250000 [12] train-rmse:480183.875000 test-rmse:536461.875000 [13] train-rmse:475527.593750 test-rmse:537323.000000 [14] train-rmse:466357.406250 test-rmse:539679.375000 Stopping. Best iteration: [11] train-rmse:481938.687500 test-rmse:535893.250000 [1] train-rmse:850665.375000 test-rmse:1423210.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:678712.937500 test-rmse:1309499.750000 [3] train-rmse:571794.750000 test-rmse:1252284.500000 [4] train-rmse:509419.937500 test-rmse:1212639.875000 [5] train-rmse:466342.343750 test-rmse:1189303.750000 [6] train-rmse:444346.406250 test-rmse:1183345.500000 [7] train-rmse:425655.593750 test-rmse:1184597.125000 [8] train-rmse:416344.718750 test-rmse:1182561.750000 [9] train-rmse:409982.531250 test-rmse:1179978.500000 [10] train-rmse:401500.343750 test-rmse:1175211.000000 [11] train-rmse:398868.875000 test-rmse:1172604.875000 [12] train-rmse:395131.281250 test-rmse:1171238.750000 [13] train-rmse:393811.406250 test-rmse:1170852.375000 [14] train-rmse:385511.562500 test-rmse:1171103.750000 [15] train-rmse:384274.156250 test-rmse:1168554.625000 [16] train-rmse:380528.906250 test-rmse:1174154.000000 [17] train-rmse:374624.000000 test-rmse:1177531.875000 [18] train-rmse:372819.968750 test-rmse:1183645.500000 Stopping. Best iteration: [15] train-rmse:384274.156250 test-rmse:1168554.625000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:873993.312500 test-rmse:991558.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:699012.687500 test-rmse:796180.062500 [3] train-rmse:591105.937500 test-rmse:697246.312500 [4] train-rmse:525850.750000 test-rmse:644607.187500 [5] train-rmse:488421.718750 test-rmse:615258.937500 [6] train-rmse:467008.656250 test-rmse:612199.125000 [7] train-rmse:455151.375000 test-rmse:609468.687500 [8] train-rmse:449432.812500 test-rmse:612327.375000 [9] train-rmse:444242.437500 test-rmse:618268.062500 [10] train-rmse:440017.093750 test-rmse:618930.437500 Stopping. Best iteration: [7] train-rmse:455151.375000 test-rmse:609468.687500 [1] train-rmse:881588.937500 test-rmse:1095465.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:701318.250000 test-rmse:958473.125000 [3] train-rmse:589902.062500 test-rmse:874334.562500 [4] train-rmse:521066.843750 test-rmse:823155.625000 [5] train-rmse:484058.156250 test-rmse:795522.750000 [6] train-rmse:463245.500000 test-rmse:769800.812500 [7] train-rmse:451755.375000 test-rmse:759738.375000 [8] train-rmse:445163.406250 test-rmse:752035.687500 [9] train-rmse:441186.000000 test-rmse:742298.062500 [10] train-rmse:439294.281250 test-rmse:731184.125000 [11] train-rmse:437406.843750 test-rmse:725015.687500 [12] train-rmse:436319.281250 test-rmse:718002.187500 [13] train-rmse:435748.375000 test-rmse:713537.000000 [14] train-rmse:433213.187500 test-rmse:710956.750000 [15] train-rmse:431953.625000 test-rmse:710476.187500 [16] train-rmse:431061.156250 test-rmse:708281.312500 [17] train-rmse:430934.468750 test-rmse:706916.187500 [18] train-rmse:430046.031250 test-rmse:706070.500000 [19] train-rmse:429899.093750 test-rmse:704927.062500 [20] train-rmse:429734.375000 test-rmse:704314.125000 [1] train-rmse:965435.687500 test-rmse:449453.437500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:769851.687500 test-rmse:344054.031250 [3] train-rmse:652464.250000 test-rmse:338274.125000 [4] train-rmse:582888.437500 test-rmse:369788.500000 [5] train-rmse:544419.000000 test-rmse:388531.062500 [6] train-rmse:523301.093750 test-rmse:414542.062500 Stopping. Best iteration: [3] train-rmse:652464.250000 test-rmse:338274.125000 [1] train-rmse:920143.375000 test-rmse:776401.562500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:729466.625000 test-rmse:626488.500000 [3] train-rmse:611711.250000 test-rmse:539216.125000 [4] train-rmse:543075.250000 test-rmse:497008.812500 [5] train-rmse:504900.593750 test-rmse:472690.250000 [6] train-rmse:481698.562500 test-rmse:459190.562500 [7] train-rmse:470461.343750 test-rmse:457263.281250 [8] train-rmse:464556.781250 test-rmse:457118.656250 [9] train-rmse:461574.500000 test-rmse:456312.968750 [10] train-rmse:457562.625000 test-rmse:456094.968750 [11] train-rmse:454519.968750 test-rmse:455759.281250 [12] train-rmse:452317.968750 test-rmse:457774.500000 [13] train-rmse:450601.062500 test-rmse:457720.781250 [14] train-rmse:449985.406250 test-rmse:457434.406250 Stopping. Best iteration: [11] train-rmse:454519.968750 test-rmse:455759.281250 [1] train-rmse:827483.500000 test-rmse:1218013.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:665092.812500 test-rmse:1047067.000000 [3] train-rmse:564467.625000 test-rmse:946889.375000 [4] train-rmse:500739.031250 test-rmse:849031.937500 [5] train-rmse:462996.000000 test-rmse:790984.000000 [6] train-rmse:442966.843750 test-rmse:757283.812500 [7] train-rmse:430291.218750 test-rmse:739340.125000 [8] train-rmse:423674.468750 test-rmse:725854.125000 [9] train-rmse:419892.531250 test-rmse:722203.875000 [10] train-rmse:416398.906250 test-rmse:716436.062500 [11] train-rmse:414820.250000 test-rmse:714621.000000 [12] train-rmse:412654.437500 test-rmse:687016.875000 [13] train-rmse:411317.687500 test-rmse:661769.750000 [14] train-rmse:410310.250000 test-rmse:660623.125000 [15] train-rmse:409661.812500 test-rmse:661650.250000 [16] train-rmse:408291.718750 test-rmse:643339.062500 [17] train-rmse:407648.625000 test-rmse:639906.500000 [18] train-rmse:406890.562500 test-rmse:640239.562500 [19] train-rmse:406288.062500 test-rmse:641339.375000 [20] train-rmse:406032.593750 test-rmse:641498.000000 Stopping. Best iteration: [17] train-rmse:407648.625000 test-rmse:639906.500000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:561356.875000 test-rmse:2561954.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:439250.750000 test-rmse:2455016.250000 [3] train-rmse:361754.250000 test-rmse:2386372.250000 [4] train-rmse:313792.687500 test-rmse:2341718.000000 [5] train-rmse:284630.687500 test-rmse:2303323.500000 [6] train-rmse:266788.687500 test-rmse:2278388.500000 [7] train-rmse:255635.906250 test-rmse:2259587.000000 [8] train-rmse:249713.265625 test-rmse:2253205.250000 [9] train-rmse:246022.312500 test-rmse:2240264.750000 [10] train-rmse:241720.109375 test-rmse:2233057.250000 [11] train-rmse:239873.781250 test-rmse:2231758.250000 [12] train-rmse:237460.921875 test-rmse:2226638.000000 [13] train-rmse:235974.500000 test-rmse:2222451.000000 [14] train-rmse:234893.906250 test-rmse:2217765.000000 [15] train-rmse:233933.125000 test-rmse:2214743.500000 [16] train-rmse:233383.390625 test-rmse:2212673.750000 [17] train-rmse:232524.125000 test-rmse:2213005.750000 [18] train-rmse:231400.906250 test-rmse:2212829.250000 [19] train-rmse:231148.828125 test-rmse:2212774.750000 Stopping. Best iteration: [16] train-rmse:233383.390625 test-rmse:2212673.750000 [1] train-rmse:599046.187500 test-rmse:1405906.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:469951.093750 test-rmse:1332122.875000 [3] train-rmse:388812.500000 test-rmse:1279256.875000 [4] train-rmse:339848.687500 test-rmse:1247424.250000 [5] train-rmse:308816.187500 test-rmse:1241191.500000 [6] train-rmse:291385.812500 test-rmse:1225484.750000 [7] train-rmse:280825.531250 test-rmse:1213955.750000 [8] train-rmse:274860.906250 test-rmse:1205108.000000 [9] train-rmse:271692.500000 test-rmse:1201046.500000 [10] train-rmse:269714.125000 test-rmse:1205289.125000 [11] train-rmse:268817.718750 test-rmse:1203839.500000 [12] train-rmse:267237.062500 test-rmse:1202383.375000 Stopping. Best iteration: [9] train-rmse:271692.500000 test-rmse:1201046.500000 [1] train-rmse:656825.500000 test-rmse:783246.062500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:513129.750000 test-rmse:737164.437500 [3] train-rmse:422936.562500 test-rmse:710702.062500 [4] train-rmse:366792.843750 test-rmse:693743.000000 [5] train-rmse:332909.593750 test-rmse:679881.250000 [6] train-rmse:313778.781250 test-rmse:676094.187500 [7] train-rmse:302857.250000 test-rmse:668926.312500 [8] train-rmse:295829.343750 test-rmse:663438.875000 [9] train-rmse:291781.562500 test-rmse:660638.562500 [10] train-rmse:289183.125000 test-rmse:657962.187500 [11] train-rmse:285329.281250 test-rmse:658005.125000 [12] train-rmse:284265.187500 test-rmse:657866.562500 [13] train-rmse:283148.093750 test-rmse:656640.937500 [14] train-rmse:282227.187500 test-rmse:655604.437500 [15] train-rmse:280849.468750 test-rmse:656584.437500 [16] train-rmse:280020.593750 test-rmse:656488.937500 [17] train-rmse:279473.093750 test-rmse:656466.062500 Stopping. Best iteration: [14] train-rmse:282227.187500 test-rmse:655604.437500 [1] train-rmse:623124.187500 test-rmse:1635561.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:485542.562500 test-rmse:1570995.500000 [3] train-rmse:397362.875000 test-rmse:1522329.250000 [4] train-rmse:343272.312500 test-rmse:1496191.500000 [5] train-rmse:311373.093750 test-rmse:1480220.500000 [6] train-rmse:292265.218750 test-rmse:1462362.875000 [7] train-rmse:281899.593750 test-rmse:1449704.250000 [8] train-rmse:276309.062500 test-rmse:1443489.375000 [9] train-rmse:271183.312500 test-rmse:1442118.875000 [10] train-rmse:269173.187500 test-rmse:1437386.250000 [11] train-rmse:267541.750000 test-rmse:1433820.375000 [12] train-rmse:266356.406250 test-rmse:1431586.125000 [13] train-rmse:264917.718750 test-rmse:1431242.000000 [14] train-rmse:264266.750000 test-rmse:1429273.500000 [15] train-rmse:263612.812500 test-rmse:1428823.125000 [16] train-rmse:262767.187500 test-rmse:1429177.500000 [17] train-rmse:262519.562500 test-rmse:1428131.125000 [18] train-rmse:262215.218750 test-rmse:1427783.250000 [19] train-rmse:261993.250000 test-rmse:1427577.375000 [20] train-rmse:261414.671875 test-rmse:1427009.500000 [1] train-rmse:584384.250000 test-rmse:1959563.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:456266.531250 test-rmse:1884724.750000 [3] train-rmse:375403.906250 test-rmse:1839155.875000 [4] train-rmse:324463.500000 test-rmse:1812210.000000 [5] train-rmse:291938.937500 test-rmse:1791962.375000 [6] train-rmse:273459.343750 test-rmse:1777246.000000 [7] train-rmse:263189.968750 test-rmse:1767400.750000 [8] train-rmse:257402.500000 test-rmse:1759914.125000 [9] train-rmse:254102.015625 test-rmse:1754910.875000 [10] train-rmse:250824.718750 test-rmse:1752093.125000 [11] train-rmse:249214.562500 test-rmse:1756768.500000 [12] train-rmse:248584.234375 test-rmse:1754677.750000 [13] train-rmse:248219.109375 test-rmse:1754441.000000 Stopping. Best iteration: [10] train-rmse:250824.718750 test-rmse:1752093.125000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:299275.750000 test-rmse:5014814.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:240094.046875 test-rmse:4992696.500000 [3] train-rmse:200235.562500 test-rmse:4990401.000000 [4] train-rmse:176070.843750 test-rmse:4988890.500000 [5] train-rmse:162154.187500 test-rmse:4987899.000000 [6] train-rmse:154384.000000 test-rmse:4987111.000000 [7] train-rmse:150132.406250 test-rmse:4986472.500000 [8] train-rmse:147993.140625 test-rmse:4985992.000000 [9] train-rmse:146689.328125 test-rmse:4985002.000000 [10] train-rmse:145932.312500 test-rmse:4984264.000000 [11] train-rmse:145495.625000 test-rmse:4983755.500000 [12] train-rmse:145249.031250 test-rmse:4983922.500000 [13] train-rmse:145097.203125 test-rmse:4984077.000000 [14] train-rmse:144993.828125 test-rmse:4983865.000000 Stopping. Best iteration: [11] train-rmse:145495.625000 test-rmse:4983755.500000 [1] train-rmse:300621.343750 test-rmse:2069281.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:240083.703125 test-rmse:2025548.500000 [3] train-rmse:201256.468750 test-rmse:1995677.125000 [4] train-rmse:178098.171875 test-rmse:1974522.750000 [5] train-rmse:164822.312500 test-rmse:1962777.875000 [6] train-rmse:157525.718750 test-rmse:1953049.500000 [7] train-rmse:153446.984375 test-rmse:1947152.125000 [8] train-rmse:151104.062500 test-rmse:1940904.000000 [9] train-rmse:149849.062500 test-rmse:1936285.500000 [10] train-rmse:149164.375000 test-rmse:1932681.125000 [11] train-rmse:148781.390625 test-rmse:1929796.000000 [12] train-rmse:148454.234375 test-rmse:1926643.125000 [13] train-rmse:148313.734375 test-rmse:1924828.875000 [14] train-rmse:148172.734375 test-rmse:1923009.875000 [15] train-rmse:148085.953125 test-rmse:1921721.500000 [16] train-rmse:148047.218750 test-rmse:1920815.250000 [17] train-rmse:148013.937500 test-rmse:1920096.000000 [18] train-rmse:147983.375000 test-rmse:1919727.000000 [19] train-rmse:147961.859375 test-rmse:1919429.750000 [20] train-rmse:147938.734375 test-rmse:1919185.500000 [1] train-rmse:281104.093750 test-rmse:3635948.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:218378.656250 test-rmse:3623551.250000 [3] train-rmse:177856.125000 test-rmse:3615315.500000 [4] train-rmse:153334.328125 test-rmse:3606057.500000 [5] train-rmse:137722.312500 test-rmse:3603762.750000 [6] train-rmse:128270.062500 test-rmse:3601848.250000 [7] train-rmse:122938.992188 test-rmse:3600435.500000 [8] train-rmse:119904.726562 test-rmse:3599498.750000 [9] train-rmse:118113.046875 test-rmse:3598778.000000 [10] train-rmse:117214.617188 test-rmse:3598230.000000 [11] train-rmse:116604.578125 test-rmse:3597894.750000 [12] train-rmse:116226.453125 test-rmse:3597609.250000 [13] train-rmse:116005.046875 test-rmse:3597407.500000 [14] train-rmse:115836.593750 test-rmse:3597501.250000 [15] train-rmse:115677.718750 test-rmse:3597672.000000 [16] train-rmse:115616.304688 test-rmse:3597660.500000 Stopping. Best iteration: [13] train-rmse:116005.046875 test-rmse:3597407.500000 [1] train-rmse:301714.562500 test-rmse:581223.687500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:237401.406250 test-rmse:546243.687500 [3] train-rmse:196248.843750 test-rmse:526956.937500 [4] train-rmse:171697.437500 test-rmse:514816.562500 [5] train-rmse:154042.937500 test-rmse:498473.906250 [6] train-rmse:143902.437500 test-rmse:492436.687500 [7] train-rmse:138105.437500 test-rmse:489306.656250 [8] train-rmse:134843.031250 test-rmse:487221.375000 [9] train-rmse:132423.953125 test-rmse:493145.250000 [10] train-rmse:131167.015625 test-rmse:500319.593750 [11] train-rmse:130434.093750 test-rmse:507400.281250 Stopping. Best iteration: [8] train-rmse:134843.031250 test-rmse:487221.375000 [1] train-rmse:300794.625000 test-rmse:1245392.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:239907.578125 test-rmse:1233317.125000 [3] train-rmse:201892.921875 test-rmse:1226691.750000 [4] train-rmse:178751.093750 test-rmse:1221423.375000 [5] train-rmse:165487.531250 test-rmse:1216742.125000 [6] train-rmse:158116.750000 test-rmse:1214430.750000 [7] train-rmse:154218.703125 test-rmse:1209701.625000 [8] train-rmse:151833.671875 test-rmse:1207873.250000 [9] train-rmse:150528.703125 test-rmse:1207379.750000 [10] train-rmse:149769.843750 test-rmse:1206383.625000 [11] train-rmse:149338.359375 test-rmse:1203489.375000 [12] train-rmse:149084.109375 test-rmse:1201118.125000 [13] train-rmse:148929.781250 test-rmse:1199501.875000 [14] train-rmse:148828.421875 test-rmse:1197904.750000 [15] train-rmse:148746.218750 test-rmse:1196635.500000 [16] train-rmse:148697.578125 test-rmse:1195732.500000 [17] train-rmse:148651.781250 test-rmse:1194920.500000 [18] train-rmse:148617.015625 test-rmse:1194319.500000 [19] train-rmse:148595.812500 test-rmse:1193786.125000 [20] train-rmse:148536.984375 test-rmse:1193300.375000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:612231.812500 test-rmse:993923.687500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:483046.187500 test-rmse:946722.625000 [3] train-rmse:403497.312500 test-rmse:930155.875000 [4] train-rmse:357174.312500 test-rmse:923143.812500 [5] train-rmse:331101.781250 test-rmse:921144.500000 [6] train-rmse:316882.906250 test-rmse:921648.687500 [7] train-rmse:309338.000000 test-rmse:922818.937500 [8] train-rmse:305508.000000 test-rmse:924097.125000 Stopping. Best iteration: [5] train-rmse:331101.781250 test-rmse:921144.500000 [1] train-rmse:594914.062500 test-rmse:1385441.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:470724.531250 test-rmse:1342846.250000 [3] train-rmse:394931.125000 test-rmse:1333752.250000 [4] train-rmse:349830.531250 test-rmse:1328555.875000 [5] train-rmse:324846.687500 test-rmse:1330085.500000 [6] train-rmse:311396.687500 test-rmse:1333587.875000 [7] train-rmse:303574.687500 test-rmse:1341718.250000 Stopping. Best iteration: [4] train-rmse:349830.531250 test-rmse:1328555.875000 [1] train-rmse:508527.031250 test-rmse:1057130.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:399813.500000 test-rmse:949710.687500 [3] train-rmse:332308.531250 test-rmse:893038.875000 [4] train-rmse:291889.062500 test-rmse:848340.437500 [5] train-rmse:269804.343750 test-rmse:819129.812500 [6] train-rmse:257055.250000 test-rmse:790726.562500 [7] train-rmse:250574.015625 test-rmse:777666.437500 [8] train-rmse:245692.437500 test-rmse:761576.562500 [9] train-rmse:243436.093750 test-rmse:747405.625000 [10] train-rmse:241581.875000 test-rmse:735138.562500 [11] train-rmse:240387.078125 test-rmse:727545.875000 [12] train-rmse:239917.531250 test-rmse:724764.437500 [13] train-rmse:239177.125000 test-rmse:728518.437500 [14] train-rmse:238988.953125 test-rmse:727767.812500 [15] train-rmse:238845.515625 test-rmse:727133.687500 Stopping. Best iteration: [12] train-rmse:239917.531250 test-rmse:724764.437500 [1] train-rmse:618688.437500 test-rmse:534749.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:488197.906250 test-rmse:458309.250000 [3] train-rmse:407845.625000 test-rmse:413427.031250 [4] train-rmse:361103.187500 test-rmse:388188.625000 [5] train-rmse:335154.781250 test-rmse:373905.937500 [6] train-rmse:321302.968750 test-rmse:366104.031250 [7] train-rmse:314048.031250 test-rmse:362931.375000 [8] train-rmse:310288.500000 test-rmse:360259.843750 [9] train-rmse:308088.343750 test-rmse:357630.375000 [10] train-rmse:307061.437500 test-rmse:356768.968750 [11] train-rmse:306431.906250 test-rmse:356154.718750 [12] train-rmse:306007.812500 test-rmse:356498.531250 [13] train-rmse:305758.343750 test-rmse:356379.656250 [14] train-rmse:305503.250000 test-rmse:355921.062500 [15] train-rmse:305435.906250 test-rmse:355372.968750 [16] train-rmse:305310.750000 test-rmse:355614.750000 [17] train-rmse:305247.968750 test-rmse:355796.312500 [18] train-rmse:305218.906250 test-rmse:355961.000000 Stopping. Best iteration: [15] train-rmse:305435.906250 test-rmse:355372.968750 [1] train-rmse:599265.687500 test-rmse:559190.437500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:475339.250000 test-rmse:448814.312500 [3] train-rmse:399153.843750 test-rmse:387549.375000 [4] train-rmse:355098.093750 test-rmse:353902.625000 [5] train-rmse:331041.687500 test-rmse:335412.468750 [6] train-rmse:317799.375000 test-rmse:327776.125000 [7] train-rmse:310258.968750 test-rmse:323576.343750 [8] train-rmse:306304.812500 test-rmse:322715.062500 [9] train-rmse:304170.500000 test-rmse:321211.843750 [10] train-rmse:303006.906250 test-rmse:320816.312500 [11] train-rmse:302186.843750 test-rmse:320651.687500 [12] train-rmse:301581.906250 test-rmse:320311.093750 [13] train-rmse:301250.656250 test-rmse:320123.593750 [14] train-rmse:300996.531250 test-rmse:319419.031250 [15] train-rmse:300905.250000 test-rmse:319878.781250 [16] train-rmse:300797.312500 test-rmse:320169.562500 [17] train-rmse:300769.625000 test-rmse:320400.187500 Stopping. Best iteration: [14] train-rmse:300996.531250 test-rmse:319419.031250
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:1116963.250000 test-rmse:3941355.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:892526.125000 test-rmse:3792074.000000 [3] train-rmse:755024.187500 test-rmse:3683078.500000 [4] train-rmse:666213.375000 test-rmse:3623405.750000 [5] train-rmse:610634.125000 test-rmse:3576449.000000 [6] train-rmse:577875.625000 test-rmse:3546674.500000 [7] train-rmse:555986.250000 test-rmse:3524814.250000 [8] train-rmse:542641.750000 test-rmse:3518425.750000 [9] train-rmse:533479.562500 test-rmse:3515919.750000 [10] train-rmse:528240.187500 test-rmse:3517170.250000 [11] train-rmse:520318.812500 test-rmse:3512249.500000 [12] train-rmse:515338.187500 test-rmse:3518847.750000 [13] train-rmse:511179.625000 test-rmse:3513922.000000 [14] train-rmse:507623.062500 test-rmse:3512971.750000 Stopping. Best iteration: [11] train-rmse:520318.812500 test-rmse:3512249.500000 [1] train-rmse:1102934.125000 test-rmse:8236239.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:877110.937500 test-rmse:8137839.500000 [3] train-rmse:733249.937500 test-rmse:8073302.000000 [4] train-rmse:645102.500000 test-rmse:8036008.000000 [5] train-rmse:590727.187500 test-rmse:7997417.500000 [6] train-rmse:558069.375000 test-rmse:7969156.500000 [7] train-rmse:535103.687500 test-rmse:7944459.500000 [8] train-rmse:519989.343750 test-rmse:7935758.000000 [9] train-rmse:513342.437500 test-rmse:7923274.500000 [10] train-rmse:507835.000000 test-rmse:7918118.500000 [11] train-rmse:502333.187500 test-rmse:7914105.000000 [12] train-rmse:496445.031250 test-rmse:7911681.000000 [13] train-rmse:492900.718750 test-rmse:7910458.000000 [14] train-rmse:490432.437500 test-rmse:7908196.500000 [15] train-rmse:486076.906250 test-rmse:7904490.500000 [16] train-rmse:483270.000000 test-rmse:7902376.000000 [17] train-rmse:481398.906250 test-rmse:7902474.000000 [18] train-rmse:479306.937500 test-rmse:7904534.000000 [19] train-rmse:477776.625000 test-rmse:7905996.000000 Stopping. Best iteration: [16] train-rmse:483270.000000 test-rmse:7902376.000000 [1] train-rmse:1093818.625000 test-rmse:27292632.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:863175.750000 test-rmse:27195640.000000 [3] train-rmse:712205.750000 test-rmse:27131716.000000 [4] train-rmse:620201.062500 test-rmse:27085858.000000 [5] train-rmse:563907.250000 test-rmse:27052800.000000 [6] train-rmse:531258.437500 test-rmse:27027874.000000 [7] train-rmse:512167.250000 test-rmse:27011120.000000 [8] train-rmse:499734.531250 test-rmse:26993184.000000 [9] train-rmse:489261.406250 test-rmse:26982958.000000 [10] train-rmse:484245.343750 test-rmse:26971598.000000 [11] train-rmse:478964.125000 test-rmse:26965374.000000 [12] train-rmse:474765.281250 test-rmse:26961622.000000 [13] train-rmse:471133.718750 test-rmse:26959570.000000 [14] train-rmse:469388.250000 test-rmse:26957920.000000 [15] train-rmse:468135.750000 test-rmse:26951772.000000 [16] train-rmse:467486.906250 test-rmse:26951000.000000 [17] train-rmse:465386.406250 test-rmse:26949020.000000 [18] train-rmse:464991.750000 test-rmse:26948582.000000 [19] train-rmse:459882.156250 test-rmse:26948466.000000 [20] train-rmse:459118.406250 test-rmse:26945384.000000 [1] train-rmse:1181745.750000 test-rmse:11644570.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:935350.750000 test-rmse:11586720.000000 [3] train-rmse:777796.437500 test-rmse:11543866.000000 [4] train-rmse:679963.250000 test-rmse:11514570.000000 [5] train-rmse:618961.125000 test-rmse:11491736.000000 [6] train-rmse:582160.000000 test-rmse:11478066.000000 [7] train-rmse:560897.875000 test-rmse:11468631.000000 [8] train-rmse:547605.750000 test-rmse:11459857.000000 [9] train-rmse:537616.187500 test-rmse:11454276.000000 [10] train-rmse:530339.312500 test-rmse:11451473.000000 [11] train-rmse:525946.312500 test-rmse:11449712.000000 [12] train-rmse:516903.687500 test-rmse:11447079.000000 [13] train-rmse:515317.375000 test-rmse:11445983.000000 [14] train-rmse:509348.812500 test-rmse:11444408.000000 [15] train-rmse:507539.687500 test-rmse:11442848.000000 [16] train-rmse:504445.187500 test-rmse:11441218.000000 [17] train-rmse:500934.812500 test-rmse:11440443.000000 [18] train-rmse:498949.375000 test-rmse:11439669.000000 [19] train-rmse:498032.031250 test-rmse:11438873.000000 [20] train-rmse:496856.468750 test-rmse:11438069.000000 [1] train-rmse:1185684.500000 test-rmse:4838319.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:934162.750000 test-rmse:4787728.000000 [3] train-rmse:776787.875000 test-rmse:4756265.000000 [4] train-rmse:675794.187500 test-rmse:4737346.000000 [5] train-rmse:616925.062500 test-rmse:4717869.500000 [6] train-rmse:581743.062500 test-rmse:4710483.000000 [7] train-rmse:558670.187500 test-rmse:4703815.000000 [8] train-rmse:543393.250000 test-rmse:4704681.000000 [9] train-rmse:534292.312500 test-rmse:4701140.000000 [10] train-rmse:528128.250000 test-rmse:4700260.000000 [11] train-rmse:523560.281250 test-rmse:4693586.500000 [12] train-rmse:517983.031250 test-rmse:4686006.000000 [13] train-rmse:516437.062500 test-rmse:4682607.000000 [14] train-rmse:513875.718750 test-rmse:4678737.000000 [15] train-rmse:511793.562500 test-rmse:4678093.500000 [16] train-rmse:509146.656250 test-rmse:4680429.500000 [17] train-rmse:507018.343750 test-rmse:4680522.500000 [18] train-rmse:503131.375000 test-rmse:4680218.000000 Stopping. Best iteration: [15] train-rmse:511793.562500 test-rmse:4678093.500000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:485378.781250 test-rmse:585216.125000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:368369.812500 test-rmse:456176.531250 [3] train-rmse:293430.593750 test-rmse:377823.468750 [4] train-rmse:247816.187500 test-rmse:334455.031250 [5] train-rmse:221408.984375 test-rmse:310534.406250 [6] train-rmse:206103.218750 test-rmse:294906.375000 [7] train-rmse:198204.781250 test-rmse:286794.875000 [8] train-rmse:193800.343750 test-rmse:283504.187500 [9] train-rmse:190757.609375 test-rmse:281440.750000 [10] train-rmse:189066.109375 test-rmse:281224.750000 [11] train-rmse:188291.250000 test-rmse:281556.343750 [12] train-rmse:187532.125000 test-rmse:280976.312500 [13] train-rmse:187221.781250 test-rmse:281359.281250 [14] train-rmse:186967.437500 test-rmse:281197.343750 [15] train-rmse:186791.234375 test-rmse:281199.656250 Stopping. Best iteration: [12] train-rmse:187532.125000 test-rmse:280976.312500 [1] train-rmse:466142.375000 test-rmse:672332.187500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:357008.843750 test-rmse:537155.625000 [3] train-rmse:288324.781250 test-rmse:451668.718750 [4] train-rmse:246831.500000 test-rmse:401554.375000 [5] train-rmse:223218.562500 test-rmse:371829.875000 [6] train-rmse:208811.812500 test-rmse:354191.843750 [7] train-rmse:201128.328125 test-rmse:342820.156250 [8] train-rmse:197125.250000 test-rmse:336135.468750 [9] train-rmse:194773.359375 test-rmse:332816.187500 [10] train-rmse:193140.687500 test-rmse:331980.687500 [11] train-rmse:192421.500000 test-rmse:328825.750000 [12] train-rmse:192039.796875 test-rmse:326792.312500 [13] train-rmse:191634.843750 test-rmse:326522.218750 [14] train-rmse:191318.859375 test-rmse:325907.062500 [15] train-rmse:191116.453125 test-rmse:325927.375000 [16] train-rmse:190976.453125 test-rmse:325535.250000 [17] train-rmse:190898.109375 test-rmse:325498.750000 [18] train-rmse:190749.437500 test-rmse:325601.031250 [19] train-rmse:190673.843750 test-rmse:326044.437500 [20] train-rmse:190598.921875 test-rmse:326199.000000 Stopping. Best iteration: [17] train-rmse:190898.109375 test-rmse:325498.750000 [1] train-rmse:498265.812500 test-rmse:557317.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:377566.531250 test-rmse:456535.500000 [3] train-rmse:300187.093750 test-rmse:390680.187500 [4] train-rmse:252959.750000 test-rmse:352501.625000 [5] train-rmse:225396.781250 test-rmse:329621.562500 [6] train-rmse:210011.437500 test-rmse:317870.343750 [7] train-rmse:201926.796875 test-rmse:311222.468750 [8] train-rmse:197542.453125 test-rmse:305740.593750 [9] train-rmse:194863.984375 test-rmse:303095.437500 [10] train-rmse:193587.187500 test-rmse:302431.937500 [11] train-rmse:192733.296875 test-rmse:301766.750000 [12] train-rmse:192237.343750 test-rmse:301642.812500 [13] train-rmse:191819.046875 test-rmse:300254.250000 [14] train-rmse:191597.640625 test-rmse:299102.093750 [15] train-rmse:191329.515625 test-rmse:299384.812500 [16] train-rmse:191163.765625 test-rmse:299437.656250 [17] train-rmse:191045.078125 test-rmse:299707.656250 Stopping. Best iteration: [14] train-rmse:191597.640625 test-rmse:299102.093750 [1] train-rmse:537389.125000 test-rmse:339424.156250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:409422.343750 test-rmse:255532.578125 [3] train-rmse:326975.468750 test-rmse:195072.390625 [4] train-rmse:277059.625000 test-rmse:166743.296875 [5] train-rmse:248014.406250 test-rmse:150970.453125 [6] train-rmse:231665.000000 test-rmse:146208.171875 [7] train-rmse:222326.468750 test-rmse:142797.406250 [8] train-rmse:217699.531250 test-rmse:142572.500000 [9] train-rmse:215034.234375 test-rmse:143461.406250 [10] train-rmse:212995.218750 test-rmse:146020.171875 [11] train-rmse:212030.046875 test-rmse:147838.468750 Stopping. Best iteration: [8] train-rmse:217699.531250 test-rmse:142572.500000 [1] train-rmse:529949.062500 test-rmse:379216.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:403866.093750 test-rmse:286390.406250 [3] train-rmse:323834.812500 test-rmse:227904.203125 [4] train-rmse:273970.687500 test-rmse:198116.656250 [5] train-rmse:245200.421875 test-rmse:182634.500000 [6] train-rmse:229030.250000 test-rmse:174874.156250 [7] train-rmse:219803.250000 test-rmse:171006.921875 [8] train-rmse:215126.062500 test-rmse:170055.781250 [9] train-rmse:212035.937500 test-rmse:169459.750000 [10] train-rmse:210519.640625 test-rmse:169003.531250 [11] train-rmse:209127.765625 test-rmse:169243.640625 [12] train-rmse:208257.812500 test-rmse:169770.750000 [13] train-rmse:207906.296875 test-rmse:169719.218750 Stopping. Best iteration: [10] train-rmse:210519.640625 test-rmse:169003.531250
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:555367.625000 test-rmse:646363.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:426044.562500 test-rmse:490667.437500 [3] train-rmse:344483.968750 test-rmse:404822.250000 [4] train-rmse:295534.625000 test-rmse:364640.125000 [5] train-rmse:267758.906250 test-rmse:349631.906250 [6] train-rmse:252745.140625 test-rmse:345515.406250 [7] train-rmse:244522.218750 test-rmse:347253.468750 [8] train-rmse:240268.109375 test-rmse:351039.312500 [9] train-rmse:238028.921875 test-rmse:353779.500000 Stopping. Best iteration: [6] train-rmse:252745.140625 test-rmse:345515.406250 [1] train-rmse:556851.062500 test-rmse:690420.875000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:430724.437500 test-rmse:550213.250000 [3] train-rmse:351666.718750 test-rmse:464192.187500 [4] train-rmse:304758.531250 test-rmse:406755.593750 [5] train-rmse:278133.968750 test-rmse:374525.125000 [6] train-rmse:263621.156250 test-rmse:354642.562500 [7] train-rmse:256029.687500 test-rmse:343005.312500 [8] train-rmse:251850.750000 test-rmse:335373.218750 [9] train-rmse:249536.906250 test-rmse:331559.531250 [10] train-rmse:247995.546875 test-rmse:329650.781250 [11] train-rmse:247285.843750 test-rmse:327790.968750 [12] train-rmse:246875.046875 test-rmse:327124.531250 [13] train-rmse:246550.687500 test-rmse:326708.281250 [14] train-rmse:246239.062500 test-rmse:325954.187500 [15] train-rmse:246098.500000 test-rmse:325969.250000 [16] train-rmse:245975.437500 test-rmse:325533.968750 [17] train-rmse:245896.093750 test-rmse:326001.468750 [18] train-rmse:245804.500000 test-rmse:326315.031250 [19] train-rmse:245728.843750 test-rmse:327297.781250 Stopping. Best iteration: [16] train-rmse:245975.437500 test-rmse:325533.968750 [1] train-rmse:575952.812500 test-rmse:643574.687500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:443207.250000 test-rmse:524738.000000 [3] train-rmse:359762.687500 test-rmse:457486.218750 [4] train-rmse:310178.562500 test-rmse:420395.968750 [5] train-rmse:282299.312500 test-rmse:397274.250000 [6] train-rmse:267052.843750 test-rmse:383751.062500 [7] train-rmse:259052.703125 test-rmse:375686.781250 [8] train-rmse:254725.187500 test-rmse:370840.937500 [9] train-rmse:252385.937500 test-rmse:368956.687500 [10] train-rmse:251187.843750 test-rmse:370181.562500 [11] train-rmse:250480.921875 test-rmse:370775.250000 [12] train-rmse:249988.625000 test-rmse:370670.875000 Stopping. Best iteration: [9] train-rmse:252385.937500 test-rmse:368956.687500 [1] train-rmse:604815.812500 test-rmse:474314.968750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:465600.281250 test-rmse:372713.281250 [3] train-rmse:378131.968750 test-rmse:314320.812500 [4] train-rmse:325446.218750 test-rmse:278359.281250 [5] train-rmse:295542.281250 test-rmse:260133.937500 [6] train-rmse:279283.218750 test-rmse:250720.609375 [7] train-rmse:270355.437500 test-rmse:246285.593750 [8] train-rmse:265646.562500 test-rmse:244081.078125 [9] train-rmse:263034.437500 test-rmse:242589.500000 [10] train-rmse:261418.718750 test-rmse:241972.671875 [11] train-rmse:260479.531250 test-rmse:241401.859375 [12] train-rmse:259991.359375 test-rmse:241711.656250 [13] train-rmse:259611.125000 test-rmse:241487.078125 [14] train-rmse:259388.984375 test-rmse:241366.906250 [15] train-rmse:259167.750000 test-rmse:241684.843750 [16] train-rmse:258996.953125 test-rmse:242013.343750 [17] train-rmse:258914.093750 test-rmse:242048.187500 Stopping. Best iteration: [14] train-rmse:259388.984375 test-rmse:241366.906250 [1] train-rmse:597153.000000 test-rmse:503776.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:460555.750000 test-rmse:395621.218750 [3] train-rmse:374567.406250 test-rmse:328977.687500 [4] train-rmse:323122.906250 test-rmse:290196.937500 [5] train-rmse:294057.750000 test-rmse:266905.875000 [6] train-rmse:278270.656250 test-rmse:253429.250000 [7] train-rmse:269772.812500 test-rmse:245142.656250 [8] train-rmse:265294.843750 test-rmse:240855.203125 [9] train-rmse:263000.437500 test-rmse:238408.265625 [10] train-rmse:261747.171875 test-rmse:237015.453125 [11] train-rmse:260856.484375 test-rmse:236776.703125 [12] train-rmse:260350.437500 test-rmse:236587.015625 [13] train-rmse:260076.734375 test-rmse:236302.640625 [14] train-rmse:259860.437500 test-rmse:236308.515625 [15] train-rmse:259745.578125 test-rmse:236362.453125 [16] train-rmse:259668.765625 test-rmse:236119.703125 [17] train-rmse:259632.734375 test-rmse:236022.171875 [18] train-rmse:259541.515625 test-rmse:236140.515625 [19] train-rmse:259481.796875 test-rmse:238061.078125 [20] train-rmse:259410.093750 test-rmse:238326.578125 Stopping. Best iteration: [17] train-rmse:259632.734375 test-rmse:236022.171875
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:389246.562500 test-rmse:369211.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:309215.875000 test-rmse:296140.875000 [3] train-rmse:251432.750000 test-rmse:249805.843750 [4] train-rmse:211728.765625 test-rmse:218711.937500 [5] train-rmse:184449.687500 test-rmse:200288.093750 [6] train-rmse:166633.687500 test-rmse:183893.203125 [7] train-rmse:154301.625000 test-rmse:173120.750000 [8] train-rmse:144984.359375 test-rmse:167702.343750 [9] train-rmse:139161.375000 test-rmse:164903.546875 [10] train-rmse:135516.078125 test-rmse:161791.468750 [11] train-rmse:133139.484375 test-rmse:160239.890625 [12] train-rmse:131704.828125 test-rmse:160294.921875 [13] train-rmse:130680.515625 test-rmse:158997.250000 [14] train-rmse:129963.648438 test-rmse:159439.328125 [15] train-rmse:127697.960938 test-rmse:162831.906250 [16] train-rmse:126028.625000 test-rmse:166703.140625 Stopping. Best iteration: [13] train-rmse:130680.515625 test-rmse:158997.250000 [1] train-rmse:423763.125000 test-rmse:134327.921875 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:333360.375000 test-rmse:97179.671875 [3] train-rmse:270630.812500 test-rmse:79181.039062 [4] train-rmse:228948.703125 test-rmse:73943.046875 [5] train-rmse:198381.984375 test-rmse:76351.335938 [6] train-rmse:178151.031250 test-rmse:81784.218750 [7] train-rmse:165108.281250 test-rmse:88543.460938 Stopping. Best iteration: [4] train-rmse:228948.703125 test-rmse:73943.046875 [1] train-rmse:386252.531250 test-rmse:339828.593750 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:305096.531250 test-rmse:232919.500000 [3] train-rmse:249492.281250 test-rmse:185010.203125 [4] train-rmse:209566.656250 test-rmse:155706.234375 [5] train-rmse:185148.812500 test-rmse:134644.640625 [6] train-rmse:164052.453125 test-rmse:128900.179688 [7] train-rmse:152777.062500 test-rmse:140128.921875 [8] train-rmse:141466.921875 test-rmse:144298.593750 [9] train-rmse:136219.140625 test-rmse:153032.468750 Stopping. Best iteration: [6] train-rmse:164052.453125 test-rmse:128900.179688 [1] train-rmse:366976.156250 test-rmse:948553.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:290539.156250 test-rmse:888528.187500 [3] train-rmse:235768.500000 test-rmse:856565.625000 [4] train-rmse:199040.578125 test-rmse:832397.312500 [5] train-rmse:175228.156250 test-rmse:814897.812500 [6] train-rmse:157804.015625 test-rmse:809131.937500 [7] train-rmse:145689.484375 test-rmse:804857.687500 [8] train-rmse:137671.718750 test-rmse:801017.312500 [9] train-rmse:132267.734375 test-rmse:797848.500000 [10] train-rmse:128108.453125 test-rmse:795684.937500 [11] train-rmse:125640.445312 test-rmse:793647.875000 [12] train-rmse:123849.929688 test-rmse:791009.812500 [13] train-rmse:122426.359375 test-rmse:788629.062500 [14] train-rmse:121669.718750 test-rmse:787973.312500 [15] train-rmse:120794.421875 test-rmse:785597.000000 [16] train-rmse:120559.476562 test-rmse:783373.125000 [17] train-rmse:120359.539062 test-rmse:781318.812500 [18] train-rmse:120229.640625 test-rmse:779748.750000 [19] train-rmse:119982.968750 test-rmse:778506.562500 [20] train-rmse:119841.773438 test-rmse:777435.562500 [1] train-rmse:352369.468750 test-rmse:493698.187500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:273591.718750 test-rmse:393268.406250 [3] train-rmse:217754.406250 test-rmse:343586.906250 [4] train-rmse:178445.937500 test-rmse:310729.406250 [5] train-rmse:150760.187500 test-rmse:292777.187500 [6] train-rmse:130019.570312 test-rmse:278267.812500 [7] train-rmse:117402.648438 test-rmse:272024.937500 [8] train-rmse:107750.382812 test-rmse:267411.562500 [9] train-rmse:100601.625000 test-rmse:262757.218750 [10] train-rmse:95230.476562 test-rmse:259960.531250 [11] train-rmse:91602.257812 test-rmse:257703.656250 [12] train-rmse:88959.492188 test-rmse:257412.906250 [13] train-rmse:87524.226562 test-rmse:258158.000000 [14] train-rmse:85673.914062 test-rmse:259595.296875 [15] train-rmse:84389.187500 test-rmse:261399.000000 Stopping. Best iteration: [12] train-rmse:88959.492188 test-rmse:257412.906250
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:197759.421875 test-rmse:117078.296875 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:154051.937500 test-rmse:66769.843750 [3] train-rmse:124926.757812 test-rmse:44574.132812 [4] train-rmse:106305.250000 test-rmse:49866.980469 [5] train-rmse:94933.296875 test-rmse:64016.632812 [6] train-rmse:88275.265625 test-rmse:77155.679688 Stopping. Best iteration: [3] train-rmse:124926.757812 test-rmse:44574.132812 [1] train-rmse:199167.546875 test-rmse:136166.265625 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:156948.156250 test-rmse:97156.679688 [3] train-rmse:128694.875000 test-rmse:72795.664062 [4] train-rmse:110513.804688 test-rmse:59629.863281 [5] train-rmse:99272.343750 test-rmse:53726.792969 [6] train-rmse:92609.718750 test-rmse:52128.730469 [7] train-rmse:88776.390625 test-rmse:52463.789062 [8] train-rmse:86610.070312 test-rmse:53427.472656 [9] train-rmse:85406.226562 test-rmse:54480.539062 Stopping. Best iteration: [6] train-rmse:92609.718750 test-rmse:52128.730469 [1] train-rmse:195234.953125 test-rmse:165874.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:154006.078125 test-rmse:128471.804688 [3] train-rmse:126368.976562 test-rmse:103912.437500 [4] train-rmse:108525.398438 test-rmse:88585.578125 [5] train-rmse:97487.679688 test-rmse:79076.867188 [6] train-rmse:90928.187500 test-rmse:73469.234375 [7] train-rmse:87138.140625 test-rmse:70249.960938 [8] train-rmse:84995.210938 test-rmse:68351.226562 [9] train-rmse:83800.351562 test-rmse:67252.789062 [10] train-rmse:83128.875000 test-rmse:66584.507812 [11] train-rmse:82757.429688 test-rmse:66182.187500 [12] train-rmse:82551.734375 test-rmse:65924.546875 [13] train-rmse:82437.929688 test-rmse:65759.296875 [14] train-rmse:82374.890625 test-rmse:65652.515625 [15] train-rmse:82339.867188 test-rmse:65579.578125 [16] train-rmse:82320.406250 test-rmse:65530.097656 [17] train-rmse:82309.570312 test-rmse:65496.625000 [18] train-rmse:82303.562500 test-rmse:65472.906250 [19] train-rmse:82300.148438 test-rmse:65456.351562 [20] train-rmse:82298.234375 test-rmse:65444.914062 [1] train-rmse:172182.234375 test-rmse:266023.406250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:139364.890625 test-rmse:224431.343750 [3] train-rmse:115669.453125 test-rmse:189134.312500 [4] train-rmse:99443.593750 test-rmse:159928.625000 [5] train-rmse:88614.000000 test-rmse:140773.890625 [6] train-rmse:81558.914062 test-rmse:129061.953125 [7] train-rmse:76977.710938 test-rmse:123408.335938 [8] train-rmse:74038.492188 test-rmse:121077.218750 [9] train-rmse:72167.882812 test-rmse:120663.804688 [10] train-rmse:70984.804688 test-rmse:121316.289062 [11] train-rmse:70238.367188 test-rmse:122481.867188 [12] train-rmse:69767.468750 test-rmse:123794.671875 Stopping. Best iteration: [9] train-rmse:72167.882812 test-rmse:120663.804688 [1] train-rmse:181727.968750 test-rmse:230105.890625 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:141047.734375 test-rmse:192462.562500 [3] train-rmse:114277.593750 test-rmse:175028.156250 [4] train-rmse:97380.125000 test-rmse:169802.015625 [5] train-rmse:86982.578125 test-rmse:171421.171875 [6] train-rmse:80717.179688 test-rmse:176777.750000 [7] train-rmse:77044.617188 test-rmse:183170.171875 Stopping. Best iteration: [4] train-rmse:97380.125000 test-rmse:169802.015625
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:246643.531250 test-rmse:235135.484375 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:188460.828125 test-rmse:180575.656250 [3] train-rmse:150791.906250 test-rmse:147605.781250 [4] train-rmse:126950.765625 test-rmse:130022.570312 [5] train-rmse:113215.804688 test-rmse:121164.125000 [6] train-rmse:105138.539062 test-rmse:117425.859375 [7] train-rmse:100186.609375 test-rmse:115717.359375 [8] train-rmse:97397.773438 test-rmse:116353.726562 [9] train-rmse:96076.109375 test-rmse:116726.476562 [10] train-rmse:95298.023438 test-rmse:118340.593750 Stopping. Best iteration: [7] train-rmse:100186.609375 test-rmse:115717.359375 [1] train-rmse:248598.578125 test-rmse:248030.953125 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:189989.218750 test-rmse:212409.562500 [3] train-rmse:152098.875000 test-rmse:185537.515625 [4] train-rmse:128695.921875 test-rmse:170356.453125 [5] train-rmse:114534.429688 test-rmse:157995.953125 [6] train-rmse:106567.210938 test-rmse:150582.953125 [7] train-rmse:102067.242188 test-rmse:146601.062500 [8] train-rmse:99543.765625 test-rmse:141828.500000 [9] train-rmse:98154.203125 test-rmse:140902.921875 [10] train-rmse:97409.484375 test-rmse:138890.546875 [11] train-rmse:96997.679688 test-rmse:137111.046875 [12] train-rmse:96731.453125 test-rmse:136844.265625 [13] train-rmse:96512.398438 test-rmse:136724.578125 [14] train-rmse:96409.671875 test-rmse:137215.515625 [15] train-rmse:96265.781250 test-rmse:136818.656250 [16] train-rmse:96177.453125 test-rmse:136552.765625 [17] train-rmse:96125.453125 test-rmse:136286.546875 [18] train-rmse:96093.546875 test-rmse:136337.500000 [19] train-rmse:96073.632812 test-rmse:136425.812500 [20] train-rmse:96062.195312 test-rmse:136533.625000 Stopping. Best iteration: [17] train-rmse:96125.453125 test-rmse:136286.546875 [1] train-rmse:238395.187500 test-rmse:277403.531250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:182528.953125 test-rmse:226365.000000 [3] train-rmse:146617.375000 test-rmse:194607.671875 [4] train-rmse:124261.734375 test-rmse:176511.187500 [5] train-rmse:110769.156250 test-rmse:166365.437500 [6] train-rmse:103081.851562 test-rmse:159812.187500 [7] train-rmse:98917.257812 test-rmse:157370.046875 [8] train-rmse:96525.484375 test-rmse:155495.187500 [9] train-rmse:95235.320312 test-rmse:154738.859375 [10] train-rmse:94459.101562 test-rmse:154534.171875 [11] train-rmse:94055.640625 test-rmse:154768.406250 [12] train-rmse:93769.523438 test-rmse:154835.625000 [13] train-rmse:93603.695312 test-rmse:154907.453125 Stopping. Best iteration: [10] train-rmse:94459.101562 test-rmse:154534.171875 [1] train-rmse:242397.484375 test-rmse:838488.375000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:182140.468750 test-rmse:828925.375000 [3] train-rmse:141549.656250 test-rmse:823712.312500 [4] train-rmse:115760.406250 test-rmse:821189.812500 [5] train-rmse:99121.710938 test-rmse:819959.062500 [6] train-rmse:88700.851562 test-rmse:818409.937500 [7] train-rmse:82993.117188 test-rmse:817079.062500 [8] train-rmse:79345.312500 test-rmse:816249.750000 [9] train-rmse:77179.648438 test-rmse:815215.812500 [10] train-rmse:76096.062500 test-rmse:814815.437500 [11] train-rmse:75490.570312 test-rmse:814542.500000 [12] train-rmse:75145.585938 test-rmse:814361.687500 [13] train-rmse:74889.898438 test-rmse:814245.937500 [14] train-rmse:74501.718750 test-rmse:814156.562500 [15] train-rmse:74283.671875 test-rmse:814101.875000 [16] train-rmse:74216.085938 test-rmse:813270.250000 [17] train-rmse:74084.406250 test-rmse:813244.937500 [18] train-rmse:74050.289062 test-rmse:812554.062500 [19] train-rmse:73973.765625 test-rmse:812768.062500 [20] train-rmse:73945.578125 test-rmse:812776.187500 [1] train-rmse:243728.406250 test-rmse:244343.171875 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:186714.531250 test-rmse:187596.968750 [3] train-rmse:150142.437500 test-rmse:155520.453125 [4] train-rmse:127557.375000 test-rmse:140138.437500 [5] train-rmse:114380.156250 test-rmse:136374.921875 [6] train-rmse:106329.757812 test-rmse:136722.828125 [7] train-rmse:102109.812500 test-rmse:138891.593750 [8] train-rmse:99517.117188 test-rmse:141818.171875 Stopping. Best iteration: [5] train-rmse:114380.156250 test-rmse:136374.921875
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:593799.187500 test-rmse:10961211.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:485922.781250 test-rmse:10948836.000000 [3] train-rmse:414100.250000 test-rmse:10901678.000000 [4] train-rmse:368071.250000 test-rmse:10898062.000000 [5] train-rmse:334717.218750 test-rmse:10871004.000000 [6] train-rmse:315536.781250 test-rmse:10851690.000000 [7] train-rmse:301666.187500 test-rmse:10851084.000000 [8] train-rmse:292908.375000 test-rmse:10850642.000000 [9] train-rmse:286708.843750 test-rmse:10850079.000000 [10] train-rmse:280880.687500 test-rmse:10849759.000000 [11] train-rmse:277019.718750 test-rmse:10849535.000000 [12] train-rmse:273677.531250 test-rmse:10849793.000000 [13] train-rmse:270485.187500 test-rmse:10849784.000000 [14] train-rmse:268543.437500 test-rmse:10850950.000000 Stopping. Best iteration: [11] train-rmse:277019.718750 test-rmse:10849535.000000 [1] train-rmse:632480.562500 test-rmse:1255833.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:519986.875000 test-rmse:1215834.750000 [3] train-rmse:445603.531250 test-rmse:1187367.625000 [4] train-rmse:395029.531250 test-rmse:1174009.750000 [5] train-rmse:364751.343750 test-rmse:1158034.125000 [6] train-rmse:344999.875000 test-rmse:1155962.125000 [7] train-rmse:326594.000000 test-rmse:1145247.500000 [8] train-rmse:316285.906250 test-rmse:1131454.750000 [9] train-rmse:307594.500000 test-rmse:1128143.375000 [10] train-rmse:303768.937500 test-rmse:1119748.125000 [11] train-rmse:300757.250000 test-rmse:1119535.375000 [12] train-rmse:298609.906250 test-rmse:1120692.375000 [13] train-rmse:294893.468750 test-rmse:1120290.750000 [14] train-rmse:292569.968750 test-rmse:1120108.750000 Stopping. Best iteration: [11] train-rmse:300757.250000 test-rmse:1119535.375000 [1] train-rmse:621282.625000 test-rmse:1483671.625000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:507418.531250 test-rmse:1415857.000000 [3] train-rmse:435533.000000 test-rmse:1371599.625000 [4] train-rmse:385408.937500 test-rmse:1355427.375000 [5] train-rmse:353640.781250 test-rmse:1348572.750000 [6] train-rmse:332401.156250 test-rmse:1342384.875000 [7] train-rmse:316667.843750 test-rmse:1339602.750000 [8] train-rmse:301706.750000 test-rmse:1334240.375000 [9] train-rmse:294625.687500 test-rmse:1336804.250000 [10] train-rmse:286280.437500 test-rmse:1334389.125000 [11] train-rmse:280708.937500 test-rmse:1333176.625000 [12] train-rmse:277284.218750 test-rmse:1331446.625000 [13] train-rmse:274985.281250 test-rmse:1333319.000000 [14] train-rmse:273467.468750 test-rmse:1332538.000000 [15] train-rmse:272026.875000 test-rmse:1332126.125000 Stopping. Best iteration: [12] train-rmse:277284.218750 test-rmse:1331446.625000 [1] train-rmse:591991.437500 test-rmse:1311677.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:482625.843750 test-rmse:1251624.375000 [3] train-rmse:412230.781250 test-rmse:1204493.250000 [4] train-rmse:364973.531250 test-rmse:1189434.875000 [5] train-rmse:335068.687500 test-rmse:1168149.125000 [6] train-rmse:312994.750000 test-rmse:1164961.250000 [7] train-rmse:297053.500000 test-rmse:1159285.750000 [8] train-rmse:287791.468750 test-rmse:1154284.875000 [9] train-rmse:279857.906250 test-rmse:1154124.750000 [10] train-rmse:274813.343750 test-rmse:1151532.750000 [11] train-rmse:269294.062500 test-rmse:1157353.625000 [12] train-rmse:264690.093750 test-rmse:1161026.875000 [13] train-rmse:260871.406250 test-rmse:1164692.125000 Stopping. Best iteration: [10] train-rmse:274813.343750 test-rmse:1151532.750000 [1] train-rmse:604701.687500 test-rmse:3029373.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:498965.250000 test-rmse:2951229.500000 [3] train-rmse:427734.656250 test-rmse:2890380.000000 [4] train-rmse:384128.437500 test-rmse:2844691.250000 [5] train-rmse:354682.468750 test-rmse:2818903.250000 [6] train-rmse:335617.843750 test-rmse:2814632.500000 [7] train-rmse:322244.593750 test-rmse:2808541.000000 [8] train-rmse:313600.187500 test-rmse:2807732.000000 [9] train-rmse:304957.750000 test-rmse:2805081.500000 [10] train-rmse:300934.187500 test-rmse:2803812.500000 [11] train-rmse:297996.812500 test-rmse:2802879.750000 [12] train-rmse:295416.875000 test-rmse:2800809.500000 [13] train-rmse:293069.781250 test-rmse:2798056.750000 [14] train-rmse:292077.968750 test-rmse:2797740.000000 [15] train-rmse:285965.093750 test-rmse:2797805.000000 [16] train-rmse:284405.000000 test-rmse:2794670.500000 [17] train-rmse:281262.875000 test-rmse:2797075.250000 [18] train-rmse:280724.187500 test-rmse:2797320.250000 [19] train-rmse:276557.906250 test-rmse:2797001.250000 Stopping. Best iteration: [16] train-rmse:284405.000000 test-rmse:2794670.500000
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:397544.187500 test-rmse:349369.687500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:305050.500000 test-rmse:259377.500000 [3] train-rmse:247058.640625 test-rmse:204336.937500 [4] train-rmse:212488.328125 test-rmse:173275.343750 [5] train-rmse:192794.593750 test-rmse:158430.171875 [6] train-rmse:182346.468750 test-rmse:151723.781250 [7] train-rmse:176747.921875 test-rmse:150323.296875 [8] train-rmse:173862.328125 test-rmse:151096.015625 [9] train-rmse:172376.093750 test-rmse:151247.453125 [10] train-rmse:171572.125000 test-rmse:151985.609375 Stopping. Best iteration: [7] train-rmse:176747.921875 test-rmse:150323.296875 [1] train-rmse:386947.218750 test-rmse:410686.312500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:296028.343750 test-rmse:322079.218750 [3] train-rmse:238327.750000 test-rmse:265898.281250 [4] train-rmse:203844.484375 test-rmse:231919.484375 [5] train-rmse:184376.484375 test-rmse:213026.062500 [6] train-rmse:173919.500000 test-rmse:202490.812500 [7] train-rmse:168473.953125 test-rmse:196548.140625 [8] train-rmse:165541.187500 test-rmse:193197.656250 [9] train-rmse:164077.953125 test-rmse:191564.812500 [10] train-rmse:163290.046875 test-rmse:190457.781250 [11] train-rmse:162843.421875 test-rmse:189850.406250 [12] train-rmse:162558.765625 test-rmse:189623.250000 [13] train-rmse:162354.203125 test-rmse:190658.859375 [14] train-rmse:162241.156250 test-rmse:190625.937500 [15] train-rmse:162153.593750 test-rmse:191517.734375 Stopping. Best iteration: [12] train-rmse:162558.765625 test-rmse:189623.250000 [1] train-rmse:393063.937500 test-rmse:378713.531250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:299288.250000 test-rmse:292734.718750 [3] train-rmse:239723.812500 test-rmse:243666.359375 [4] train-rmse:204040.343750 test-rmse:217704.890625 [5] train-rmse:183622.031250 test-rmse:206267.218750 [6] train-rmse:172659.625000 test-rmse:200951.140625 [7] train-rmse:166817.359375 test-rmse:199766.734375 [8] train-rmse:163629.390625 test-rmse:199023.718750 [9] train-rmse:162023.281250 test-rmse:198676.984375 [10] train-rmse:161076.234375 test-rmse:198778.625000 [11] train-rmse:160594.328125 test-rmse:199875.984375 [12] train-rmse:160335.234375 test-rmse:200200.953125 Stopping. Best iteration: [9] train-rmse:162023.281250 test-rmse:198676.984375 [1] train-rmse:385399.718750 test-rmse:414722.750000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:295658.281250 test-rmse:327149.312500 [3] train-rmse:239362.625000 test-rmse:271218.343750 [4] train-rmse:205661.343750 test-rmse:236673.937500 [5] train-rmse:186802.062500 test-rmse:213615.859375 [6] train-rmse:176516.781250 test-rmse:202036.750000 [7] train-rmse:171137.000000 test-rmse:194467.125000 [8] train-rmse:168204.125000 test-rmse:192366.953125 [9] train-rmse:166698.609375 test-rmse:191307.000000 [10] train-rmse:165896.953125 test-rmse:190727.531250 [11] train-rmse:165517.984375 test-rmse:190169.015625 [12] train-rmse:165276.093750 test-rmse:190598.406250 [13] train-rmse:165150.812500 test-rmse:190856.890625 [14] train-rmse:165049.156250 test-rmse:190984.343750 Stopping. Best iteration: [11] train-rmse:165517.984375 test-rmse:190169.015625 [1] train-rmse:387290.343750 test-rmse:612894.687500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:296996.250000 test-rmse:547677.750000 [3] train-rmse:240480.656250 test-rmse:512106.875000 [4] train-rmse:206605.171875 test-rmse:491232.781250 [5] train-rmse:187589.921875 test-rmse:480106.093750 [6] train-rmse:177364.390625 test-rmse:474179.656250 [7] train-rmse:171896.031250 test-rmse:470865.843750 [8] train-rmse:169125.406250 test-rmse:468872.000000 [9] train-rmse:167624.968750 test-rmse:467236.031250 [10] train-rmse:166845.281250 test-rmse:466472.312500 [11] train-rmse:166435.781250 test-rmse:466092.281250 [12] train-rmse:166231.890625 test-rmse:465908.375000 [13] train-rmse:166126.656250 test-rmse:465830.937500 [14] train-rmse:166068.937500 test-rmse:465811.437500 [15] train-rmse:166037.468750 test-rmse:465817.812500 [16] train-rmse:166015.156250 test-rmse:465847.062500 [17] train-rmse:166006.093750 test-rmse:465852.968750 Stopping. Best iteration: [14] train-rmse:166068.937500 test-rmse:465811.437500
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:451294.750000 test-rmse:468841.156250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:344705.406250 test-rmse:360665.781250 [3] train-rmse:277495.125000 test-rmse:295440.968750 [4] train-rmse:236582.859375 test-rmse:252412.328125 [5] train-rmse:213383.859375 test-rmse:230659.390625 [6] train-rmse:200751.218750 test-rmse:220758.500000 [7] train-rmse:193794.265625 test-rmse:216507.046875 [8] train-rmse:190197.734375 test-rmse:216085.406250 [9] train-rmse:188374.031250 test-rmse:217056.234375 [10] train-rmse:187449.390625 test-rmse:218309.312500 [11] train-rmse:186832.156250 test-rmse:218342.171875 Stopping. Best iteration: [8] train-rmse:190197.734375 test-rmse:216085.406250 [1] train-rmse:455925.000000 test-rmse:440687.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:347835.375000 test-rmse:333171.281250 [3] train-rmse:279224.968750 test-rmse:268143.281250 [4] train-rmse:237855.328125 test-rmse:235013.187500 [5] train-rmse:214330.484375 test-rmse:220719.562500 [6] train-rmse:201585.437500 test-rmse:215887.328125 [7] train-rmse:194732.578125 test-rmse:215171.984375 [8] train-rmse:191213.484375 test-rmse:216311.296875 [9] train-rmse:189314.687500 test-rmse:217759.000000 [10] train-rmse:188387.828125 test-rmse:219043.656250 Stopping. Best iteration: [7] train-rmse:194732.578125 test-rmse:215171.984375 [1] train-rmse:460777.968750 test-rmse:436471.031250 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:351996.218750 test-rmse:338546.687500 [3] train-rmse:282894.750000 test-rmse:275596.562500 [4] train-rmse:241096.968750 test-rmse:242567.406250 [5] train-rmse:217110.609375 test-rmse:224713.500000 [6] train-rmse:204134.109375 test-rmse:216143.812500 [7] train-rmse:197167.546875 test-rmse:212210.781250 [8] train-rmse:193551.734375 test-rmse:210427.750000 [9] train-rmse:191729.203125 test-rmse:209593.125000 [10] train-rmse:190769.703125 test-rmse:209938.968750 [11] train-rmse:190270.906250 test-rmse:210130.484375 [12] train-rmse:190016.218750 test-rmse:210439.703125 Stopping. Best iteration: [9] train-rmse:191729.203125 test-rmse:209593.125000 [1] train-rmse:457727.187500 test-rmse:454725.062500 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:351779.031250 test-rmse:355294.218750 [3] train-rmse:283398.156250 test-rmse:290322.312500 [4] train-rmse:241353.531250 test-rmse:244016.671875 [5] train-rmse:217420.937500 test-rmse:219510.015625 [6] train-rmse:204369.421875 test-rmse:207923.437500 [7] train-rmse:197525.218750 test-rmse:203482.234375 [8] train-rmse:194039.000000 test-rmse:201173.515625 [9] train-rmse:192239.812500 test-rmse:200859.343750 [10] train-rmse:191349.546875 test-rmse:201072.421875 [11] train-rmse:190828.687500 test-rmse:200910.000000 [12] train-rmse:190571.968750 test-rmse:201305.093750 Stopping. Best iteration: [9] train-rmse:192239.812500 test-rmse:200859.343750 [1] train-rmse:450206.343750 test-rmse:478370.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:344573.250000 test-rmse:369745.312500 [3] train-rmse:277766.687500 test-rmse:304572.625000 [4] train-rmse:237105.171875 test-rmse:249090.250000 [5] train-rmse:213759.015625 test-rmse:223788.265625 [6] train-rmse:201007.593750 test-rmse:217174.578125 [7] train-rmse:194303.828125 test-rmse:219177.484375 [8] train-rmse:190767.640625 test-rmse:221228.218750 [9] train-rmse:188932.562500 test-rmse:224703.000000 Stopping. Best iteration: [6] train-rmse:201007.593750 test-rmse:217174.578125
| borough | b_class_group | Feature | avg_gain |
|---|---|---|---|
| <int> | <chr> | <chr> | <dbl> |
| 1 | c | residentialunits_group | 0.624942282 |
| 1 | c | zipcode | 0.241305336 |
| 1 | c | commercialunits_group | 0.063362111 |
| 1 | c | building_clusters | 0.035358679 |
| 1 | c | address_encoded | 0.018489023 |
| 1 | c | highly_commercial | 0.010980340 |
| 1 | c | taxclass_present | 0.005562229 |
| 1 | d | residentialunits_group | 0.699375098 |
| 1 | d | zipcode | 0.180517556 |
| 1 | d | address_encoded | 0.065001727 |
| 1 | d | highly_commercial | 0.041224830 |
| 1 | d | commercialunits_group | 0.011869984 |
| 1 | d | building_clusters | 0.002010806 |
| 1 | r | zipcode | 0.564797556 |
| 1 | r | address_encoded | 0.137072897 |
| 1 | r | highly_commercial | 0.081475627 |
| 1 | r | building_clusters | 0.054435241 |
| 1 | r | residentialunits_group | 0.076636173 |
| 1 | r | onlycommercial | 0.038730392 |
| 1 | r | taxclass_present | 0.025937855 |
| 1 | r | commercialunits_group | 0.020914258 |
| 1 | other | zipcode | 0.664168857 |
| 1 | other | address_encoded | 0.078734601 |
| 1 | other | commercialunits_group | 0.097746088 |
| 1 | other | residentialunits_group | 0.047601436 |
| 1 | other | highly_commercial | 0.045394080 |
| 1 | other | onlycommercial | 0.045529440 |
| 1 | other | taxclass_present | 0.019648760 |
| 1 | other | building_clusters | 0.001470922 |
| 1 | a | zipcode | 0.578086884 |
| ... | ... | ... | ... |
| 5 | c | zipcode | 0.0864559968 |
| 5 | c | address_encoded | 0.0533524300 |
| 5 | c | highly_commercial | 0.0042959804 |
| 5 | c | residentialunits_group | 0.0154377945 |
| 5 | d | address_encoded | 0.9134361040 |
| 5 | d | zipcode | 0.0853566261 |
| 5 | d | residentialunits_group | 0.0228482439 |
| 5 | r | zipcode | 0.5039611046 |
| 5 | r | highly_commercial | 0.1444360386 |
| 5 | r | taxclass_present | 0.2178543946 |
| 5 | r | address_encoded | 0.1292355103 |
| 5 | r | residentialunits_group | 0.0334001597 |
| 5 | other | taxclass_present | 0.3682126787 |
| 5 | other | zipcode | 0.3016206615 |
| 5 | other | address_encoded | 0.0984954282 |
| 5 | other | commercialunits_group | 0.1120990682 |
| 5 | other | building_clusters | 0.0165831881 |
| 5 | other | residentialunits_group | 0.0706425413 |
| 5 | other | onlycommercial | 0.0117814143 |
| 5 | other | highly_commercial | 0.0205650198 |
| 5 | a | building_clusters | 0.7731867781 |
| 5 | a | zipcode | 0.1803933955 |
| 5 | a | address_encoded | 0.0449844694 |
| 5 | a | highly_commercial | 0.0011241572 |
| 5 | a | commercialunits_group | 0.0005186664 |
| 5 | b | zipcode | 0.9022087105 |
| 5 | b | address_encoded | 0.0753858007 |
| 5 | b | highly_commercial | 0.0049486685 |
| 5 | b | building_clusters | 0.0174084737 |
| 5 | b | commercialunits_group | 0.0010380804 |
[1] "overall test rmse:"
colnames(dt)
feature_list = c( "zipcode","commercialunits_group","residentialunits_group","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
,"grosssquarefeet_log_filled")
train_target = "saleprice_log_wo"
test_target = "saleprice_log"
fit_bb_wo = model_xgboost_partial_wo(feature_list,train_target,test_target,chunk_no = 5)
pred_table_bb_wo = fit_bb_wo[[1]]
imp_table_bb_wo = fit_bb_wo[[2]]
print("overall test rmse:")
calc_rmse(pred_table_bb_wo$pred,pred_table_bb_wo$actual)
calc_rmse(pred_table_bb_wo[actual < 20000000]$pred,pred_table_bb_wo[actual < 20000000]$actual)
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.552166 test-rmse:9.356132 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.722876 test-rmse:6.622382 [3] train-rmse:4.747572 test-rmse:4.668015 [4] train-rmse:3.375171 test-rmse:3.313744 [5] train-rmse:2.425585 test-rmse:2.358979 [6] train-rmse:1.774989 test-rmse:1.711883 [7] train-rmse:1.336462 test-rmse:1.356502 [8] train-rmse:1.047213 test-rmse:1.084626 [9] train-rmse:0.858564 test-rmse:0.947166 [10] train-rmse:0.741735 test-rmse:0.939152 [11] train-rmse:0.674714 test-rmse:0.905602 [12] train-rmse:0.629354 test-rmse:0.952983 [13] train-rmse:0.606832 test-rmse:0.943788 [14] train-rmse:0.588239 test-rmse:0.937474 Stopping. Best iteration: [11] train-rmse:0.674714 test-rmse:0.905602 [1] train-rmse:9.478835 test-rmse:9.564686 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.667587 test-rmse:6.718262 [3] train-rmse:4.707224 test-rmse:4.720729 [4] train-rmse:3.344986 test-rmse:3.364698 [5] train-rmse:2.403982 test-rmse:2.442912 [6] train-rmse:1.762113 test-rmse:1.825949 [7] train-rmse:1.330310 test-rmse:1.425878 [8] train-rmse:1.040580 test-rmse:1.176795 [9] train-rmse:0.860549 test-rmse:1.065130 [10] train-rmse:0.744804 test-rmse:0.963619 [11] train-rmse:0.671060 test-rmse:0.936239 [12] train-rmse:0.624974 test-rmse:0.920738 [13] train-rmse:0.600370 test-rmse:0.916448 [14] train-rmse:0.582166 test-rmse:0.898973 [15] train-rmse:0.567312 test-rmse:0.910398 [16] train-rmse:0.560309 test-rmse:0.910730 [17] train-rmse:0.557477 test-rmse:0.907668 Stopping. Best iteration: [14] train-rmse:0.582166 test-rmse:0.898973 [1] train-rmse:9.441979 test-rmse:9.616797 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.641438 test-rmse:6.672493 [3] train-rmse:4.686681 test-rmse:4.653553 [4] train-rmse:3.326824 test-rmse:3.253396 [5] train-rmse:2.388308 test-rmse:2.336989 [6] train-rmse:1.746792 test-rmse:1.744442 [7] train-rmse:1.314811 test-rmse:1.403401 [8] train-rmse:1.032364 test-rmse:1.244767 [9] train-rmse:0.856735 test-rmse:1.182218 [10] train-rmse:0.738158 test-rmse:1.158234 [11] train-rmse:0.673105 test-rmse:1.161487 [12] train-rmse:0.639436 test-rmse:1.167776 [13] train-rmse:0.612510 test-rmse:1.181409 Stopping. Best iteration: [10] train-rmse:0.738158 test-rmse:1.158234 [1] train-rmse:9.523540 test-rmse:9.453149 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.702629 test-rmse:6.758835 [3] train-rmse:4.733945 test-rmse:4.872200 [4] train-rmse:3.366111 test-rmse:3.554830 [5] train-rmse:2.417407 test-rmse:2.609901 [6] train-rmse:1.769898 test-rmse:1.995337 [7] train-rmse:1.332850 test-rmse:1.549869 [8] train-rmse:1.043571 test-rmse:1.255481 [9] train-rmse:0.858716 test-rmse:1.059095 [10] train-rmse:0.746041 test-rmse:0.954922 [11] train-rmse:0.677938 test-rmse:0.882763 [12] train-rmse:0.640610 test-rmse:0.837086 [13] train-rmse:0.610188 test-rmse:0.829642 [14] train-rmse:0.597134 test-rmse:0.800070 [15] train-rmse:0.584387 test-rmse:0.782789 [16] train-rmse:0.572621 test-rmse:0.778480 [17] train-rmse:0.569531 test-rmse:0.774385 [18] train-rmse:0.561992 test-rmse:0.776008 [19] train-rmse:0.558641 test-rmse:0.771995 [20] train-rmse:0.551513 test-rmse:0.769922 [1] train-rmse:9.512008 test-rmse:9.501356 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.694768 test-rmse:6.705690 [3] train-rmse:4.729495 test-rmse:4.814570 [4] train-rmse:3.363333 test-rmse:3.493036 [5] train-rmse:2.417689 test-rmse:2.605846 [6] train-rmse:1.769895 test-rmse:2.015341 [7] train-rmse:1.332017 test-rmse:1.590481 [8] train-rmse:1.043322 test-rmse:1.303013 [9] train-rmse:0.858734 test-rmse:1.120085 [10] train-rmse:0.746227 test-rmse:1.007002 [11] train-rmse:0.674747 test-rmse:0.964091 [12] train-rmse:0.633668 test-rmse:0.937540 [13] train-rmse:0.606551 test-rmse:0.916867 [14] train-rmse:0.589961 test-rmse:0.897095 [15] train-rmse:0.581614 test-rmse:0.880676 [16] train-rmse:0.571546 test-rmse:0.872719 [17] train-rmse:0.566340 test-rmse:0.864673 [18] train-rmse:0.559651 test-rmse:0.873245 [19] train-rmse:0.555084 test-rmse:0.887620 [20] train-rmse:0.549447 test-rmse:0.887249 Stopping. Best iteration: [17] train-rmse:0.566340 test-rmse:0.864673
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.308808 test-rmse:9.383113 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.544896 test-rmse:6.614523 [3] train-rmse:4.619780 test-rmse:4.701146 [4] train-rmse:3.283414 test-rmse:3.297549 [5] train-rmse:2.364345 test-rmse:2.340504 [6] train-rmse:1.741933 test-rmse:1.737769 [7] train-rmse:1.331625 test-rmse:1.359652 [8] train-rmse:1.071890 test-rmse:1.147955 [9] train-rmse:0.915371 test-rmse:1.058255 [10] train-rmse:0.827546 test-rmse:1.039479 [11] train-rmse:0.779598 test-rmse:1.019622 [12] train-rmse:0.754620 test-rmse:1.041331 [13] train-rmse:0.739674 test-rmse:1.049211 [14] train-rmse:0.733205 test-rmse:1.050514 Stopping. Best iteration: [11] train-rmse:0.779598 test-rmse:1.019622 [1] train-rmse:9.363310 test-rmse:9.041458 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.582729 test-rmse:6.321845 [3] train-rmse:4.646228 test-rmse:4.427988 [4] train-rmse:3.303603 test-rmse:3.141913 [5] train-rmse:2.380207 test-rmse:2.266481 [6] train-rmse:1.754949 test-rmse:1.674063 [7] train-rmse:1.344823 test-rmse:1.315859 [8] train-rmse:1.085296 test-rmse:1.074807 [9] train-rmse:0.928580 test-rmse:0.946648 [10] train-rmse:0.839990 test-rmse:0.884046 [11] train-rmse:0.790579 test-rmse:0.854938 [12] train-rmse:0.764304 test-rmse:0.868182 [13] train-rmse:0.750759 test-rmse:0.870523 [14] train-rmse:0.742879 test-rmse:0.871562 Stopping. Best iteration: [11] train-rmse:0.790579 test-rmse:0.854938 [1] train-rmse:9.302869 test-rmse:9.416537 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.538085 test-rmse:6.672723 [3] train-rmse:4.611725 test-rmse:4.768060 [4] train-rmse:3.275373 test-rmse:3.457406 [5] train-rmse:2.355922 test-rmse:2.565162 [6] train-rmse:1.729765 test-rmse:1.978313 [7] train-rmse:1.314567 test-rmse:1.613304 [8] train-rmse:1.051835 test-rmse:1.362979 [9] train-rmse:0.892107 test-rmse:1.225176 [10] train-rmse:0.797006 test-rmse:1.133520 [11] train-rmse:0.745358 test-rmse:1.079631 [12] train-rmse:0.717727 test-rmse:1.038554 [13] train-rmse:0.703017 test-rmse:1.016486 [14] train-rmse:0.696184 test-rmse:0.999907 [15] train-rmse:0.690445 test-rmse:0.987679 [16] train-rmse:0.688583 test-rmse:0.980482 [17] train-rmse:0.687299 test-rmse:0.975441 [18] train-rmse:0.685854 test-rmse:0.971789 [19] train-rmse:0.685040 test-rmse:0.970787 [20] train-rmse:0.684004 test-rmse:0.974760 [1] train-rmse:9.301092 test-rmse:9.426648 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.538011 test-rmse:6.620306 [3] train-rmse:4.613751 test-rmse:4.668963 [4] train-rmse:3.278494 test-rmse:3.417400 [5] train-rmse:2.358362 test-rmse:2.559797 [6] train-rmse:1.731834 test-rmse:1.989039 [7] train-rmse:1.318081 test-rmse:1.603511 [8] train-rmse:1.053796 test-rmse:1.381554 [9] train-rmse:0.894067 test-rmse:1.237553 [10] train-rmse:0.801471 test-rmse:1.159165 [11] train-rmse:0.751378 test-rmse:1.115417 [12] train-rmse:0.721755 test-rmse:1.090738 [13] train-rmse:0.706824 test-rmse:1.084269 [14] train-rmse:0.699618 test-rmse:1.077083 [15] train-rmse:0.692911 test-rmse:1.073156 [16] train-rmse:0.689101 test-rmse:1.079258 [17] train-rmse:0.687322 test-rmse:1.089956 [18] train-rmse:0.685579 test-rmse:1.093579 Stopping. Best iteration: [15] train-rmse:0.692911 test-rmse:1.073156 [1] train-rmse:9.315903 test-rmse:9.328956 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.549110 test-rmse:6.549678 [3] train-rmse:4.622064 test-rmse:4.615303 [4] train-rmse:3.287051 test-rmse:3.258649 [5] train-rmse:2.368877 test-rmse:2.353732 [6] train-rmse:1.744591 test-rmse:1.731309 [7] train-rmse:1.331876 test-rmse:1.341222 [8] train-rmse:1.073249 test-rmse:1.093227 [9] train-rmse:0.914446 test-rmse:0.968879 [10] train-rmse:0.824516 test-rmse:0.897431 [11] train-rmse:0.773999 test-rmse:0.865590 [12] train-rmse:0.749228 test-rmse:0.847772 [13] train-rmse:0.734297 test-rmse:0.843755 [14] train-rmse:0.727042 test-rmse:0.842057 [15] train-rmse:0.722163 test-rmse:0.840893 [16] train-rmse:0.717268 test-rmse:0.840628 [17] train-rmse:0.715650 test-rmse:0.843389 [18] train-rmse:0.714345 test-rmse:0.842133 [19] train-rmse:0.713951 test-rmse:0.842014 Stopping. Best iteration: [16] train-rmse:0.717268 test-rmse:0.840628
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.673265 test-rmse:9.796436 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.802246 test-rmse:6.833838 [3] train-rmse:4.801968 test-rmse:4.793856 [4] train-rmse:3.413309 test-rmse:3.388548 [5] train-rmse:2.457033 test-rmse:2.428964 [6] train-rmse:1.806588 test-rmse:1.801284 [7] train-rmse:1.379129 test-rmse:1.397477 [8] train-rmse:1.107509 test-rmse:1.139159 [9] train-rmse:0.941203 test-rmse:0.999441 [10] train-rmse:0.850360 test-rmse:0.927643 [11] train-rmse:0.800234 test-rmse:0.896791 [12] train-rmse:0.774095 test-rmse:0.878277 [13] train-rmse:0.756734 test-rmse:0.874320 [14] train-rmse:0.748696 test-rmse:0.872969 [15] train-rmse:0.742806 test-rmse:0.872657 [16] train-rmse:0.735493 test-rmse:0.869797 [17] train-rmse:0.734097 test-rmse:0.872587 [18] train-rmse:0.733137 test-rmse:0.873269 [19] train-rmse:0.730338 test-rmse:0.873143 Stopping. Best iteration: [16] train-rmse:0.735493 test-rmse:0.869797 [1] train-rmse:9.719263 test-rmse:9.611497 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.833141 test-rmse:6.689017 [3] train-rmse:4.823213 test-rmse:4.676952 [4] train-rmse:3.426982 test-rmse:3.292643 [5] train-rmse:2.466306 test-rmse:2.356006 [6] train-rmse:1.813524 test-rmse:1.716486 [7] train-rmse:1.384199 test-rmse:1.330534 [8] train-rmse:1.109642 test-rmse:1.140573 [9] train-rmse:0.946683 test-rmse:1.007900 [10] train-rmse:0.853235 test-rmse:0.945103 [11] train-rmse:0.801660 test-rmse:0.909105 [12] train-rmse:0.769956 test-rmse:0.896023 [13] train-rmse:0.756020 test-rmse:0.892790 [14] train-rmse:0.745777 test-rmse:0.892472 [15] train-rmse:0.741601 test-rmse:0.893386 [16] train-rmse:0.739542 test-rmse:0.894472 [17] train-rmse:0.736924 test-rmse:0.891282 [18] train-rmse:0.729430 test-rmse:0.897502 [19] train-rmse:0.727337 test-rmse:0.898717 [20] train-rmse:0.726722 test-rmse:0.899833 Stopping. Best iteration: [17] train-rmse:0.736924 test-rmse:0.891282 [1] train-rmse:9.731908 test-rmse:9.629003 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.841656 test-rmse:6.711530 [3] train-rmse:4.827944 test-rmse:4.749248 [4] train-rmse:3.428957 test-rmse:3.375889 [5] train-rmse:2.463249 test-rmse:2.431102 [6] train-rmse:1.806435 test-rmse:1.824820 [7] train-rmse:1.372363 test-rmse:1.456907 [8] train-rmse:1.095354 test-rmse:1.231766 [9] train-rmse:0.924022 test-rmse:1.136209 [10] train-rmse:0.828543 test-rmse:1.082415 [11] train-rmse:0.776728 test-rmse:1.058236 [12] train-rmse:0.748230 test-rmse:1.047290 [13] train-rmse:0.731896 test-rmse:1.047742 [14] train-rmse:0.721928 test-rmse:1.052752 [15] train-rmse:0.717530 test-rmse:1.053518 Stopping. Best iteration: [12] train-rmse:0.748230 test-rmse:1.047290 [1] train-rmse:9.684103 test-rmse:9.881186 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.809634 test-rmse:6.994687 [3] train-rmse:4.807853 test-rmse:4.990734 [4] train-rmse:3.416237 test-rmse:3.576003 [5] train-rmse:2.458659 test-rmse:2.620875 [6] train-rmse:1.810498 test-rmse:1.971269 [7] train-rmse:1.383022 test-rmse:1.531062 [8] train-rmse:1.107733 test-rmse:1.231748 [9] train-rmse:0.944933 test-rmse:1.068197 [10] train-rmse:0.846539 test-rmse:0.965020 [11] train-rmse:0.795055 test-rmse:0.906469 [12] train-rmse:0.764638 test-rmse:0.881607 [13] train-rmse:0.749485 test-rmse:0.877756 [14] train-rmse:0.741998 test-rmse:0.866860 [15] train-rmse:0.733064 test-rmse:0.861744 [16] train-rmse:0.728737 test-rmse:0.868356 [17] train-rmse:0.725537 test-rmse:0.864675 [18] train-rmse:0.724261 test-rmse:0.864685 Stopping. Best iteration: [15] train-rmse:0.733064 test-rmse:0.861744 [1] train-rmse:9.729442 test-rmse:9.650084 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.840380 test-rmse:6.865303 [3] train-rmse:4.826741 test-rmse:4.917699 [4] train-rmse:3.428102 test-rmse:3.533483 [5] train-rmse:2.463439 test-rmse:2.582223 [6] train-rmse:1.808738 test-rmse:1.958544 [7] train-rmse:1.373474 test-rmse:1.551651 [8] train-rmse:1.097831 test-rmse:1.302286 [9] train-rmse:0.932662 test-rmse:1.152110 [10] train-rmse:0.838360 test-rmse:1.066770 [11] train-rmse:0.783933 test-rmse:1.025148 [12] train-rmse:0.755991 test-rmse:1.002330 [13] train-rmse:0.738404 test-rmse:0.995470 [14] train-rmse:0.729828 test-rmse:0.991708 [15] train-rmse:0.720939 test-rmse:0.998511 [16] train-rmse:0.718356 test-rmse:0.998467 [17] train-rmse:0.716468 test-rmse:0.998694 Stopping. Best iteration: [14] train-rmse:0.729828 test-rmse:0.991708
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:10.252530 test-rmse:11.562238 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.239427 test-rmse:8.371754 [3] train-rmse:5.133748 test-rmse:6.148461 [4] train-rmse:3.666218 test-rmse:4.547652 [5] train-rmse:2.649230 test-rmse:3.465000 [6] train-rmse:1.943855 test-rmse:2.785538 [7] train-rmse:1.445124 test-rmse:2.334625 [8] train-rmse:1.108617 test-rmse:2.033537 [9] train-rmse:0.875724 test-rmse:1.848188 [10] train-rmse:0.720943 test-rmse:1.723239 [11] train-rmse:0.612823 test-rmse:1.652541 [12] train-rmse:0.527882 test-rmse:1.606399 [13] train-rmse:0.494447 test-rmse:1.572577 [14] train-rmse:0.451491 test-rmse:1.547798 [15] train-rmse:0.431478 test-rmse:1.532591 [16] train-rmse:0.410619 test-rmse:1.527663 [17] train-rmse:0.403394 test-rmse:1.515424 [18] train-rmse:0.386989 test-rmse:1.502576 [19] train-rmse:0.370323 test-rmse:1.503995 [20] train-rmse:0.353590 test-rmse:1.502083 [1] train-rmse:10.314149 test-rmse:10.997541 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.281901 test-rmse:7.789572 [3] train-rmse:5.168149 test-rmse:5.580784 [4] train-rmse:3.701112 test-rmse:4.048347 [5] train-rmse:2.677182 test-rmse:3.012615 [6] train-rmse:1.969568 test-rmse:2.296321 [7] train-rmse:1.484900 test-rmse:1.860495 [8] train-rmse:1.145316 test-rmse:1.574048 [9] train-rmse:0.921260 test-rmse:1.403625 [10] train-rmse:0.762831 test-rmse:1.311208 [11] train-rmse:0.653569 test-rmse:1.255257 [12] train-rmse:0.568541 test-rmse:1.244594 [13] train-rmse:0.533320 test-rmse:1.226772 [14] train-rmse:0.480019 test-rmse:1.243620 [15] train-rmse:0.441475 test-rmse:1.251389 [16] train-rmse:0.424032 test-rmse:1.265917 Stopping. Best iteration: [13] train-rmse:0.533320 test-rmse:1.226772 [1] train-rmse:10.315773 test-rmse:11.048160 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.284554 test-rmse:7.841098 [3] train-rmse:5.166527 test-rmse:5.610078 [4] train-rmse:3.697185 test-rmse:4.119605 [5] train-rmse:2.673973 test-rmse:3.127750 [6] train-rmse:1.977923 test-rmse:2.523930 [7] train-rmse:1.483919 test-rmse:2.076898 [8] train-rmse:1.149557 test-rmse:1.822768 [9] train-rmse:0.935123 test-rmse:1.632330 [10] train-rmse:0.777311 test-rmse:1.502118 [11] train-rmse:0.672271 test-rmse:1.411672 [12] train-rmse:0.602241 test-rmse:1.351709 [13] train-rmse:0.538441 test-rmse:1.347083 [14] train-rmse:0.508179 test-rmse:1.318266 [15] train-rmse:0.465968 test-rmse:1.306015 [16] train-rmse:0.432605 test-rmse:1.299057 [17] train-rmse:0.400770 test-rmse:1.306831 [18] train-rmse:0.386286 test-rmse:1.315972 [19] train-rmse:0.363905 test-rmse:1.307185 Stopping. Best iteration: [16] train-rmse:0.432605 test-rmse:1.299057 [1] train-rmse:10.842122 test-rmse:7.365639 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.653814 test-rmse:4.478764 [3] train-rmse:5.430436 test-rmse:3.455967 [4] train-rmse:3.893165 test-rmse:2.397119 [5] train-rmse:2.835679 test-rmse:2.113634 [6] train-rmse:2.089568 test-rmse:1.750842 [7] train-rmse:1.569852 test-rmse:1.502872 [8] train-rmse:1.214864 test-rmse:1.377869 [9] train-rmse:0.979818 test-rmse:1.300912 [10] train-rmse:0.836641 test-rmse:1.225409 [11] train-rmse:0.741625 test-rmse:1.174799 [12] train-rmse:0.653853 test-rmse:1.131047 [13] train-rmse:0.596430 test-rmse:1.100131 [14] train-rmse:0.555201 test-rmse:1.092937 [15] train-rmse:0.528763 test-rmse:1.089234 [16] train-rmse:0.495540 test-rmse:1.103290 [17] train-rmse:0.455488 test-rmse:1.085511 [18] train-rmse:0.435790 test-rmse:1.085479 [19] train-rmse:0.420682 test-rmse:1.046349 [20] train-rmse:0.416070 test-rmse:1.050530 [1] train-rmse:10.321153 test-rmse:10.949098 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.293215 test-rmse:7.891206 [3] train-rmse:5.178936 test-rmse:5.743303 [4] train-rmse:3.705577 test-rmse:4.270451 [5] train-rmse:2.681798 test-rmse:3.237941 [6] train-rmse:1.971655 test-rmse:2.517576 [7] train-rmse:1.495427 test-rmse:2.134144 [8] train-rmse:1.148649 test-rmse:1.787634 [9] train-rmse:0.914394 test-rmse:1.601302 [10] train-rmse:0.758751 test-rmse:1.504894 [11] train-rmse:0.649736 test-rmse:1.432270 [12] train-rmse:0.574674 test-rmse:1.407617 [13] train-rmse:0.526375 test-rmse:1.399732 [14] train-rmse:0.476499 test-rmse:1.396730 [15] train-rmse:0.455297 test-rmse:1.400843 [16] train-rmse:0.432390 test-rmse:1.408819 [17] train-rmse:0.399321 test-rmse:1.412406 Stopping. Best iteration: [14] train-rmse:0.476499 test-rmse:1.396730
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:10.617598 test-rmse:10.605449 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.533655 test-rmse:7.515969 [3] train-rmse:5.377080 test-rmse:5.351726 [4] train-rmse:3.872694 test-rmse:3.820570 [5] train-rmse:2.838739 test-rmse:2.768799 [6] train-rmse:2.106102 test-rmse:2.089733 [7] train-rmse:1.556733 test-rmse:1.597951 [8] train-rmse:1.198617 test-rmse:1.258500 [9] train-rmse:0.911337 test-rmse:1.004423 [10] train-rmse:0.736856 test-rmse:0.863307 [11] train-rmse:0.585473 test-rmse:0.757332 [12] train-rmse:0.479954 test-rmse:0.673265 [13] train-rmse:0.388814 test-rmse:0.597686 [14] train-rmse:0.321838 test-rmse:0.543745 [15] train-rmse:0.272831 test-rmse:0.509134 [16] train-rmse:0.236980 test-rmse:0.482242 [17] train-rmse:0.209809 test-rmse:0.463777 [18] train-rmse:0.190487 test-rmse:0.451366 [19] train-rmse:0.175419 test-rmse:0.430244 [20] train-rmse:0.164896 test-rmse:0.425777 [1] train-rmse:10.814026 test-rmse:9.228526 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.652287 test-rmse:6.085682 [3] train-rmse:5.433616 test-rmse:3.903089 [4] train-rmse:3.884176 test-rmse:2.428269 [5] train-rmse:2.807911 test-rmse:1.523705 [6] train-rmse:2.060585 test-rmse:1.115992 [7] train-rmse:1.552759 test-rmse:1.051362 [8] train-rmse:1.159281 test-rmse:1.074435 [9] train-rmse:0.880813 test-rmse:1.143272 [10] train-rmse:0.675537 test-rmse:1.173891 Stopping. Best iteration: [7] train-rmse:1.552759 test-rmse:1.051362 [1] train-rmse:10.537584 test-rmse:11.109623 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.470594 test-rmse:8.048811 [3] train-rmse:5.324022 test-rmse:5.908852 [4] train-rmse:3.825353 test-rmse:4.399584 [5] train-rmse:2.777023 test-rmse:3.333388 [6] train-rmse:2.027566 test-rmse:2.575851 [7] train-rmse:1.489929 test-rmse:2.070570 [8] train-rmse:1.109854 test-rmse:1.741962 [9] train-rmse:0.852225 test-rmse:1.547887 [10] train-rmse:0.651250 test-rmse:1.426618 [11] train-rmse:0.505634 test-rmse:1.362741 [12] train-rmse:0.402555 test-rmse:1.331323 [13] train-rmse:0.323519 test-rmse:1.310702 [14] train-rmse:0.266260 test-rmse:1.308562 [15] train-rmse:0.223202 test-rmse:1.298935 [16] train-rmse:0.190988 test-rmse:1.308542 [17] train-rmse:0.162384 test-rmse:1.308503 [18] train-rmse:0.146497 test-rmse:1.313072 Stopping. Best iteration: [15] train-rmse:0.223202 test-rmse:1.298935 [1] train-rmse:10.546200 test-rmse:11.042486 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.487599 test-rmse:7.957868 [3] train-rmse:5.351235 test-rmse:5.784975 [4] train-rmse:3.865595 test-rmse:4.231537 [5] train-rmse:2.828450 test-rmse:3.161040 [6] train-rmse:2.110218 test-rmse:2.370679 [7] train-rmse:1.569285 test-rmse:1.782998 [8] train-rmse:1.182035 test-rmse:1.376954 [9] train-rmse:0.931917 test-rmse:1.073219 [10] train-rmse:0.730452 test-rmse:0.829610 [11] train-rmse:0.597713 test-rmse:0.647501 [12] train-rmse:0.488031 test-rmse:0.546211 [13] train-rmse:0.401719 test-rmse:0.457979 [14] train-rmse:0.338954 test-rmse:0.412077 [15] train-rmse:0.291400 test-rmse:0.403708 [16] train-rmse:0.254996 test-rmse:0.392421 [17] train-rmse:0.224265 test-rmse:0.384465 [18] train-rmse:0.207808 test-rmse:0.383698 [19] train-rmse:0.189101 test-rmse:0.381138 [20] train-rmse:0.173811 test-rmse:0.394771 [1] train-rmse:10.548205 test-rmse:11.030745 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.489059 test-rmse:7.945662 [3] train-rmse:5.352331 test-rmse:5.772517 [4] train-rmse:3.866421 test-rmse:4.218976 [5] train-rmse:2.845528 test-rmse:3.113548 [6] train-rmse:2.121238 test-rmse:2.377486 [7] train-rmse:1.570217 test-rmse:1.711187 [8] train-rmse:1.176477 test-rmse:1.243814 [9] train-rmse:0.922505 test-rmse:1.046816 [10] train-rmse:0.758261 test-rmse:0.879612 [11] train-rmse:0.604433 test-rmse:0.756195 [12] train-rmse:0.488600 test-rmse:0.695258 [13] train-rmse:0.407582 test-rmse:0.657454 [14] train-rmse:0.342293 test-rmse:0.631492 [15] train-rmse:0.294762 test-rmse:0.614778 [16] train-rmse:0.260802 test-rmse:0.600684 [17] train-rmse:0.232693 test-rmse:0.597238 [18] train-rmse:0.212554 test-rmse:0.598902 [19] train-rmse:0.195555 test-rmse:0.591296 [20] train-rmse:0.183751 test-rmse:0.590636
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:10.200396 test-rmse:10.714079 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.233291 test-rmse:7.734805 [3] train-rmse:5.154616 test-rmse:5.638537 [4] train-rmse:3.708273 test-rmse:4.166899 [5] train-rmse:2.707027 test-rmse:3.169872 [6] train-rmse:2.014689 test-rmse:2.320785 [7] train-rmse:1.518589 test-rmse:1.921857 [8] train-rmse:1.151302 test-rmse:1.558379 [9] train-rmse:0.909797 test-rmse:1.359416 [10] train-rmse:0.718242 test-rmse:1.228186 [11] train-rmse:0.576865 test-rmse:1.161580 [12] train-rmse:0.471716 test-rmse:1.101583 [13] train-rmse:0.394776 test-rmse:1.063918 [14] train-rmse:0.332600 test-rmse:1.021199 [15] train-rmse:0.284340 test-rmse:0.982043 [16] train-rmse:0.243018 test-rmse:0.969078 [17] train-rmse:0.221152 test-rmse:0.967646 [18] train-rmse:0.190811 test-rmse:0.958000 [19] train-rmse:0.165617 test-rmse:0.952884 [20] train-rmse:0.139285 test-rmse:0.944430 [1] train-rmse:10.376463 test-rmse:9.621131 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.355875 test-rmse:6.586658 [3] train-rmse:5.239548 test-rmse:4.451729 [4] train-rmse:3.766570 test-rmse:2.954970 [5] train-rmse:2.740635 test-rmse:1.924736 [6] train-rmse:2.004516 test-rmse:1.226265 [7] train-rmse:1.494570 test-rmse:0.886985 [8] train-rmse:1.114321 test-rmse:0.752389 [9] train-rmse:0.860758 test-rmse:0.661693 [10] train-rmse:0.680278 test-rmse:0.623527 [11] train-rmse:0.554951 test-rmse:0.639348 [12] train-rmse:0.442426 test-rmse:0.617881 [13] train-rmse:0.357995 test-rmse:0.640834 [14] train-rmse:0.296165 test-rmse:0.656836 [15] train-rmse:0.250850 test-rmse:0.673588 Stopping. Best iteration: [12] train-rmse:0.442426 test-rmse:0.617881 [1] train-rmse:10.386724 test-rmse:9.457172 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.354051 test-rmse:6.460222 [3] train-rmse:5.225576 test-rmse:4.388751 [4] train-rmse:3.739174 test-rmse:2.995137 [5] train-rmse:2.703747 test-rmse:2.234076 [6] train-rmse:1.982025 test-rmse:1.776787 [7] train-rmse:1.473539 test-rmse:1.500758 [8] train-rmse:1.124068 test-rmse:1.404738 [9] train-rmse:0.861820 test-rmse:1.415939 [10] train-rmse:0.684733 test-rmse:1.394268 [11] train-rmse:0.559065 test-rmse:1.398060 [12] train-rmse:0.448132 test-rmse:1.401404 [13] train-rmse:0.364551 test-rmse:1.407229 Stopping. Best iteration: [10] train-rmse:0.684733 test-rmse:1.394268 [1] train-rmse:10.157991 test-rmse:10.985187 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.199400 test-rmse:8.015477 [3] train-rmse:5.125866 test-rmse:5.925558 [4] train-rmse:3.681809 test-rmse:4.457442 [5] train-rmse:2.681837 test-rmse:3.328676 [6] train-rmse:1.989829 test-rmse:2.518005 [7] train-rmse:1.490848 test-rmse:1.968711 [8] train-rmse:1.146737 test-rmse:1.519730 [9] train-rmse:0.907468 test-rmse:1.248863 [10] train-rmse:0.735479 test-rmse:1.012193 [11] train-rmse:0.623784 test-rmse:0.851596 [12] train-rmse:0.511641 test-rmse:0.773754 [13] train-rmse:0.433490 test-rmse:0.703655 [14] train-rmse:0.384928 test-rmse:0.659552 [15] train-rmse:0.329029 test-rmse:0.629026 [16] train-rmse:0.298772 test-rmse:0.605189 [17] train-rmse:0.252810 test-rmse:0.581188 [18] train-rmse:0.221194 test-rmse:0.576138 [19] train-rmse:0.197277 test-rmse:0.567601 [20] train-rmse:0.177710 test-rmse:0.562572 [1] train-rmse:10.251273 test-rmse:10.422952 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.266932 test-rmse:7.436914 [3] train-rmse:5.175201 test-rmse:5.342409 [4] train-rmse:3.718461 test-rmse:3.881097 [5] train-rmse:2.701907 test-rmse:3.010656 [6] train-rmse:1.997215 test-rmse:2.316911 [7] train-rmse:1.492390 test-rmse:1.825289 [8] train-rmse:1.149426 test-rmse:1.540065 [9] train-rmse:0.888328 test-rmse:1.258601 [10] train-rmse:0.722362 test-rmse:1.072108 [11] train-rmse:0.576604 test-rmse:0.993441 [12] train-rmse:0.472826 test-rmse:0.948092 [13] train-rmse:0.390030 test-rmse:0.930938 [14] train-rmse:0.324794 test-rmse:0.934638 [15] train-rmse:0.276599 test-rmse:0.942245 [16] train-rmse:0.241493 test-rmse:0.926527 [17] train-rmse:0.209544 test-rmse:0.932900 [18] train-rmse:0.179011 test-rmse:0.940089 [19] train-rmse:0.154383 test-rmse:0.941104 Stopping. Best iteration: [16] train-rmse:0.241493 test-rmse:0.926527
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.005816 test-rmse:9.060042 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.329307 test-rmse:6.392803 [3] train-rmse:4.457426 test-rmse:4.520964 [4] train-rmse:3.151157 test-rmse:3.224723 [5] train-rmse:2.243558 test-rmse:2.328691 [6] train-rmse:1.618381 test-rmse:1.711690 [7] train-rmse:1.189465 test-rmse:1.313379 [8] train-rmse:0.905394 test-rmse:1.061535 [9] train-rmse:0.709430 test-rmse:0.893069 [10] train-rmse:0.584268 test-rmse:0.811150 [11] train-rmse:0.502502 test-rmse:0.768993 [12] train-rmse:0.452386 test-rmse:0.738160 [13] train-rmse:0.423678 test-rmse:0.725463 [14] train-rmse:0.399158 test-rmse:0.722873 [15] train-rmse:0.386088 test-rmse:0.717101 [16] train-rmse:0.368940 test-rmse:0.719976 [17] train-rmse:0.357557 test-rmse:0.726094 [18] train-rmse:0.348108 test-rmse:0.722428 Stopping. Best iteration: [15] train-rmse:0.386088 test-rmse:0.717101 [1] train-rmse:9.012336 test-rmse:8.962632 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.334097 test-rmse:6.301795 [3] train-rmse:4.461183 test-rmse:4.466663 [4] train-rmse:3.154772 test-rmse:3.185925 [5] train-rmse:2.246598 test-rmse:2.309329 [6] train-rmse:1.621237 test-rmse:1.764598 [7] train-rmse:1.195230 test-rmse:1.400580 [8] train-rmse:0.906401 test-rmse:1.192399 [9] train-rmse:0.718321 test-rmse:1.076165 [10] train-rmse:0.591462 test-rmse:1.023084 [11] train-rmse:0.512159 test-rmse:0.994847 [12] train-rmse:0.465091 test-rmse:0.996390 [13] train-rmse:0.430643 test-rmse:0.995670 [14] train-rmse:0.411800 test-rmse:0.999657 Stopping. Best iteration: [11] train-rmse:0.512159 test-rmse:0.994847 [1] train-rmse:8.993429 test-rmse:9.061958 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.322291 test-rmse:6.376548 [3] train-rmse:4.455416 test-rmse:4.498289 [4] train-rmse:3.152215 test-rmse:3.239403 [5] train-rmse:2.248213 test-rmse:2.358430 [6] train-rmse:1.625638 test-rmse:1.797462 [7] train-rmse:1.198292 test-rmse:1.420693 [8] train-rmse:0.912630 test-rmse:1.187880 [9] train-rmse:0.719114 test-rmse:1.043532 [10] train-rmse:0.593583 test-rmse:0.953788 [11] train-rmse:0.516424 test-rmse:0.913940 [12] train-rmse:0.469298 test-rmse:0.894662 [13] train-rmse:0.440496 test-rmse:0.888960 [14] train-rmse:0.416217 test-rmse:0.887832 [15] train-rmse:0.405772 test-rmse:0.882654 [16] train-rmse:0.392592 test-rmse:0.882687 [17] train-rmse:0.380177 test-rmse:0.884200 [18] train-rmse:0.371666 test-rmse:0.888199 Stopping. Best iteration: [15] train-rmse:0.405772 test-rmse:0.882654 [1] train-rmse:9.013486 test-rmse:9.013264 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.334933 test-rmse:6.360031 [3] train-rmse:4.461701 test-rmse:4.508011 [4] train-rmse:3.153377 test-rmse:3.218155 [5] train-rmse:2.244022 test-rmse:2.339933 [6] train-rmse:1.615573 test-rmse:1.742333 [7] train-rmse:1.187536 test-rmse:1.354523 [8] train-rmse:0.895329 test-rmse:1.102884 [9] train-rmse:0.696855 test-rmse:0.949066 [10] train-rmse:0.575894 test-rmse:0.855213 [11] train-rmse:0.489275 test-rmse:0.811562 [12] train-rmse:0.445404 test-rmse:0.774265 [13] train-rmse:0.407980 test-rmse:0.765737 [14] train-rmse:0.388201 test-rmse:0.757841 [15] train-rmse:0.374196 test-rmse:0.750490 [16] train-rmse:0.361746 test-rmse:0.751403 [17] train-rmse:0.349874 test-rmse:0.749366 [18] train-rmse:0.334248 test-rmse:0.750074 [19] train-rmse:0.331980 test-rmse:0.747775 [20] train-rmse:0.323928 test-rmse:0.751337 [1] train-rmse:9.017771 test-rmse:8.975226 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.339009 test-rmse:6.312791 [3] train-rmse:4.465899 test-rmse:4.465977 [4] train-rmse:3.158694 test-rmse:3.182086 [5] train-rmse:2.250326 test-rmse:2.298997 [6] train-rmse:1.626159 test-rmse:1.699177 [7] train-rmse:1.199037 test-rmse:1.306298 [8] train-rmse:0.912201 test-rmse:1.044503 [9] train-rmse:0.724675 test-rmse:0.887990 [10] train-rmse:0.602382 test-rmse:0.806674 [11] train-rmse:0.534372 test-rmse:0.761800 [12] train-rmse:0.478012 test-rmse:0.753478 [13] train-rmse:0.442460 test-rmse:0.759971 [14] train-rmse:0.424060 test-rmse:0.756914 [15] train-rmse:0.412839 test-rmse:0.754974 Stopping. Best iteration: [12] train-rmse:0.478012 test-rmse:0.753478
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.308707 test-rmse:7.995579 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.843086 test-rmse:5.509439 [3] train-rmse:4.120651 test-rmse:3.878995 [4] train-rmse:2.921028 test-rmse:2.745684 [5] train-rmse:2.088940 test-rmse:1.978658 [6] train-rmse:1.517419 test-rmse:1.472867 [7] train-rmse:1.132399 test-rmse:1.147541 [8] train-rmse:0.876916 test-rmse:1.017746 [9] train-rmse:0.715474 test-rmse:0.933058 [10] train-rmse:0.617117 test-rmse:0.906138 [11] train-rmse:0.563661 test-rmse:0.891540 [12] train-rmse:0.533632 test-rmse:0.893118 [13] train-rmse:0.516583 test-rmse:0.894983 [14] train-rmse:0.507574 test-rmse:0.897302 Stopping. Best iteration: [11] train-rmse:0.563661 test-rmse:0.891540 [1] train-rmse:8.242745 test-rmse:8.464622 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.800102 test-rmse:5.980728 [3] train-rmse:4.094497 test-rmse:4.343854 [4] train-rmse:2.906346 test-rmse:3.289078 [5] train-rmse:2.083255 test-rmse:2.514565 [6] train-rmse:1.516345 test-rmse:1.981035 [7] train-rmse:1.130579 test-rmse:1.614352 [8] train-rmse:0.878060 test-rmse:1.404437 [9] train-rmse:0.717970 test-rmse:1.261452 [10] train-rmse:0.619158 test-rmse:1.169732 [11] train-rmse:0.563059 test-rmse:1.111534 [12] train-rmse:0.532288 test-rmse:1.092880 [13] train-rmse:0.515219 test-rmse:1.078917 [14] train-rmse:0.506671 test-rmse:1.071623 [15] train-rmse:0.502028 test-rmse:1.066394 [16] train-rmse:0.499578 test-rmse:1.060285 [17] train-rmse:0.498210 test-rmse:1.057652 [18] train-rmse:0.497466 test-rmse:1.053990 [19] train-rmse:0.496770 test-rmse:1.055386 [20] train-rmse:0.495717 test-rmse:1.052921 [1] train-rmse:8.251737 test-rmse:8.343327 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.804506 test-rmse:5.858419 [3] train-rmse:4.095828 test-rmse:4.125178 [4] train-rmse:2.904492 test-rmse:2.920673 [5] train-rmse:2.080170 test-rmse:2.092581 [6] train-rmse:1.514830 test-rmse:1.532221 [7] train-rmse:1.128512 test-rmse:1.161159 [8] train-rmse:0.873349 test-rmse:0.916882 [9] train-rmse:0.713362 test-rmse:0.779058 [10] train-rmse:0.613205 test-rmse:0.708278 [11] train-rmse:0.556368 test-rmse:0.671470 [12] train-rmse:0.527137 test-rmse:0.656334 [13] train-rmse:0.509210 test-rmse:0.649623 [14] train-rmse:0.498568 test-rmse:0.646021 [15] train-rmse:0.492974 test-rmse:0.645559 [16] train-rmse:0.489695 test-rmse:0.645724 [17] train-rmse:0.486475 test-rmse:0.646137 [18] train-rmse:0.482090 test-rmse:0.646383 Stopping. Best iteration: [15] train-rmse:0.492974 test-rmse:0.645559 [1] train-rmse:8.217504 test-rmse:8.540228 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.780064 test-rmse:6.066843 [3] train-rmse:4.077964 test-rmse:4.340991 [4] train-rmse:2.892839 test-rmse:3.167921 [5] train-rmse:2.074111 test-rmse:2.332174 [6] train-rmse:1.510039 test-rmse:1.738172 [7] train-rmse:1.129435 test-rmse:1.331894 [8] train-rmse:0.880082 test-rmse:1.069887 [9] train-rmse:0.716823 test-rmse:0.895740 [10] train-rmse:0.616682 test-rmse:0.783200 [11] train-rmse:0.559955 test-rmse:0.728444 [12] train-rmse:0.529470 test-rmse:0.693440 [13] train-rmse:0.513228 test-rmse:0.670997 [14] train-rmse:0.502697 test-rmse:0.661837 [15] train-rmse:0.496348 test-rmse:0.656002 [16] train-rmse:0.489733 test-rmse:0.651697 [17] train-rmse:0.484528 test-rmse:0.648745 [18] train-rmse:0.483272 test-rmse:0.645356 [19] train-rmse:0.481938 test-rmse:0.644723 [20] train-rmse:0.481519 test-rmse:0.644707 [1] train-rmse:8.272318 test-rmse:8.236525 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.819273 test-rmse:5.834726 [3] train-rmse:4.106527 test-rmse:4.130485 [4] train-rmse:2.913360 test-rmse:2.990531 [5] train-rmse:2.085336 test-rmse:2.193932 [6] train-rmse:1.516238 test-rmse:1.636190 [7] train-rmse:1.133779 test-rmse:1.277822 [8] train-rmse:0.883175 test-rmse:1.065198 [9] train-rmse:0.720018 test-rmse:0.938013 [10] train-rmse:0.619601 test-rmse:0.879039 [11] train-rmse:0.559084 test-rmse:0.856188 [12] train-rmse:0.527969 test-rmse:0.841258 [13] train-rmse:0.511456 test-rmse:0.830660 [14] train-rmse:0.502043 test-rmse:0.820557 [15] train-rmse:0.495966 test-rmse:0.819017 [16] train-rmse:0.492442 test-rmse:0.819320 [17] train-rmse:0.490323 test-rmse:0.820106 [18] train-rmse:0.488766 test-rmse:0.816267 [19] train-rmse:0.485027 test-rmse:0.818535 [20] train-rmse:0.483698 test-rmse:0.814196
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.166001 test-rmse:8.608320 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.748271 test-rmse:6.198362 [3] train-rmse:4.059164 test-rmse:4.436376 [4] train-rmse:2.883997 test-rmse:3.222721 [5] train-rmse:2.065591 test-rmse:2.444430 [6] train-rmse:1.501045 test-rmse:1.912941 [7] train-rmse:1.111783 test-rmse:1.473505 [8] train-rmse:0.855812 test-rmse:1.178049 [9] train-rmse:0.686731 test-rmse:1.046639 [10] train-rmse:0.574915 test-rmse:0.983302 [11] train-rmse:0.505780 test-rmse:0.966221 [12] train-rmse:0.464258 test-rmse:0.950104 [13] train-rmse:0.438752 test-rmse:0.960423 [14] train-rmse:0.424515 test-rmse:0.980784 [15] train-rmse:0.416702 test-rmse:0.997203 Stopping. Best iteration: [12] train-rmse:0.464258 test-rmse:0.950104 [1] train-rmse:8.282216 test-rmse:7.886954 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.833778 test-rmse:5.440467 [3] train-rmse:4.124823 test-rmse:3.853125 [4] train-rmse:2.938355 test-rmse:2.757293 [5] train-rmse:2.110865 test-rmse:2.001395 [6] train-rmse:1.537245 test-rmse:1.503270 [7] train-rmse:1.147894 test-rmse:1.179196 [8] train-rmse:0.889236 test-rmse:0.980100 [9] train-rmse:0.710020 test-rmse:0.873817 [10] train-rmse:0.594415 test-rmse:0.815297 [11] train-rmse:0.519678 test-rmse:0.794534 [12] train-rmse:0.475010 test-rmse:0.779081 [13] train-rmse:0.447168 test-rmse:0.778240 [14] train-rmse:0.430419 test-rmse:0.766156 [15] train-rmse:0.419978 test-rmse:0.756670 [16] train-rmse:0.413654 test-rmse:0.751183 [17] train-rmse:0.409798 test-rmse:0.746831 [18] train-rmse:0.407057 test-rmse:0.748620 [19] train-rmse:0.405450 test-rmse:0.745937 [20] train-rmse:0.404034 test-rmse:0.744340 [1] train-rmse:8.308361 test-rmse:7.727080 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.853351 test-rmse:5.266701 [3] train-rmse:4.141809 test-rmse:3.657744 [4] train-rmse:2.954791 test-rmse:2.542138 [5] train-rmse:2.127976 test-rmse:1.812340 [6] train-rmse:1.559373 test-rmse:1.323957 [7] train-rmse:1.175382 test-rmse:1.024309 [8] train-rmse:0.904071 test-rmse:0.842046 [9] train-rmse:0.725639 test-rmse:0.737265 [10] train-rmse:0.606038 test-rmse:0.684164 [11] train-rmse:0.531629 test-rmse:0.657636 [12] train-rmse:0.484369 test-rmse:0.646024 [13] train-rmse:0.455114 test-rmse:0.641658 [14] train-rmse:0.436940 test-rmse:0.640177 [15] train-rmse:0.425219 test-rmse:0.639930 [16] train-rmse:0.418362 test-rmse:0.640103 [17] train-rmse:0.413581 test-rmse:0.640480 [18] train-rmse:0.410545 test-rmse:0.641055 Stopping. Best iteration: [15] train-rmse:0.425219 test-rmse:0.639930 [1] train-rmse:8.265890 test-rmse:7.993636 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.822973 test-rmse:5.551984 [3] train-rmse:4.120212 test-rmse:3.898516 [4] train-rmse:2.939185 test-rmse:2.751915 [5] train-rmse:2.114054 test-rmse:1.956149 [6] train-rmse:1.542855 test-rmse:1.399089 [7] train-rmse:1.154710 test-rmse:1.048021 [8] train-rmse:0.896670 test-rmse:0.839187 [9] train-rmse:0.716587 test-rmse:0.730252 [10] train-rmse:0.598995 test-rmse:0.676672 [11] train-rmse:0.522635 test-rmse:0.659217 [12] train-rmse:0.474465 test-rmse:0.659120 [13] train-rmse:0.444317 test-rmse:0.671171 [14] train-rmse:0.426042 test-rmse:0.687794 [15] train-rmse:0.414990 test-rmse:0.705066 Stopping. Best iteration: [12] train-rmse:0.474465 test-rmse:0.659120 [1] train-rmse:8.127623 test-rmse:8.814696 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.715819 test-rmse:6.425705 [3] train-rmse:4.032134 test-rmse:4.607714 [4] train-rmse:2.857960 test-rmse:3.382540 [5] train-rmse:2.036949 test-rmse:2.594609 [6] train-rmse:1.469908 test-rmse:2.061536 [7] train-rmse:1.073396 test-rmse:1.769878 [8] train-rmse:0.801421 test-rmse:1.586017 [9] train-rmse:0.617396 test-rmse:1.456706 [10] train-rmse:0.494420 test-rmse:1.396310 [11] train-rmse:0.416700 test-rmse:1.358381 [12] train-rmse:0.367418 test-rmse:1.346037 [13] train-rmse:0.336692 test-rmse:1.329104 [14] train-rmse:0.318218 test-rmse:1.325111 [15] train-rmse:0.307336 test-rmse:1.321491 [16] train-rmse:0.301459 test-rmse:1.323229 [17] train-rmse:0.297469 test-rmse:1.325670 [18] train-rmse:0.295118 test-rmse:1.329406 Stopping. Best iteration: [15] train-rmse:0.307336 test-rmse:1.321491
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.252199 test-rmse:9.289011 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.536190 test-rmse:6.580436 [3] train-rmse:4.643437 test-rmse:4.711421 [4] train-rmse:3.329158 test-rmse:3.438282 [5] train-rmse:2.426591 test-rmse:2.613271 [6] train-rmse:1.803934 test-rmse:2.092507 [7] train-rmse:1.383180 test-rmse:1.741940 [8] train-rmse:1.105686 test-rmse:1.537906 [9] train-rmse:0.931345 test-rmse:1.416814 [10] train-rmse:0.828820 test-rmse:1.344403 [11] train-rmse:0.744991 test-rmse:1.289222 [12] train-rmse:0.689370 test-rmse:1.274323 [13] train-rmse:0.662548 test-rmse:1.258236 [14] train-rmse:0.627278 test-rmse:1.246390 [15] train-rmse:0.608967 test-rmse:1.249854 [16] train-rmse:0.601110 test-rmse:1.245131 [17] train-rmse:0.577686 test-rmse:1.243901 [18] train-rmse:0.560124 test-rmse:1.236742 [19] train-rmse:0.555476 test-rmse:1.235650 [20] train-rmse:0.548621 test-rmse:1.237885 [1] train-rmse:9.231738 test-rmse:9.435885 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.520951 test-rmse:6.717286 [3] train-rmse:4.633475 test-rmse:4.846131 [4] train-rmse:3.324351 test-rmse:3.533014 [5] train-rmse:2.427636 test-rmse:2.626682 [6] train-rmse:1.809790 test-rmse:2.080164 [7] train-rmse:1.387662 test-rmse:1.752421 [8] train-rmse:1.106144 test-rmse:1.508631 [9] train-rmse:0.930238 test-rmse:1.360105 [10] train-rmse:0.821196 test-rmse:1.285354 [11] train-rmse:0.729978 test-rmse:1.238517 [12] train-rmse:0.667550 test-rmse:1.229177 [13] train-rmse:0.625657 test-rmse:1.211424 [14] train-rmse:0.608606 test-rmse:1.195622 [15] train-rmse:0.589838 test-rmse:1.190996 [16] train-rmse:0.570797 test-rmse:1.190822 [17] train-rmse:0.561234 test-rmse:1.188438 [18] train-rmse:0.542291 test-rmse:1.180809 [19] train-rmse:0.528077 test-rmse:1.181989 [20] train-rmse:0.523243 test-rmse:1.179514 [1] train-rmse:9.279930 test-rmse:9.167756 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.557322 test-rmse:6.502502 [3] train-rmse:4.661143 test-rmse:4.657519 [4] train-rmse:3.348444 test-rmse:3.390910 [5] train-rmse:2.441995 test-rmse:2.507992 [6] train-rmse:1.822349 test-rmse:1.895577 [7] train-rmse:1.393439 test-rmse:1.517202 [8] train-rmse:1.111614 test-rmse:1.311802 [9] train-rmse:0.933593 test-rmse:1.229087 [10] train-rmse:0.823044 test-rmse:1.193386 [11] train-rmse:0.756334 test-rmse:1.167541 [12] train-rmse:0.708870 test-rmse:1.156769 [13] train-rmse:0.664682 test-rmse:1.162609 [14] train-rmse:0.646086 test-rmse:1.157925 [15] train-rmse:0.631366 test-rmse:1.168533 Stopping. Best iteration: [12] train-rmse:0.708870 test-rmse:1.156769 [1] train-rmse:9.172651 test-rmse:9.867160 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.484277 test-rmse:7.077303 [3] train-rmse:4.612891 test-rmse:5.158493 [4] train-rmse:3.315099 test-rmse:3.774591 [5] train-rmse:2.427638 test-rmse:2.838191 [6] train-rmse:1.814293 test-rmse:2.199532 [7] train-rmse:1.385960 test-rmse:1.785603 [8] train-rmse:1.110651 test-rmse:1.499170 [9] train-rmse:0.920678 test-rmse:1.318061 [10] train-rmse:0.805731 test-rmse:1.205915 [11] train-rmse:0.735506 test-rmse:1.113958 [12] train-rmse:0.688578 test-rmse:1.073919 [13] train-rmse:0.657518 test-rmse:1.030261 [14] train-rmse:0.639796 test-rmse:1.005500 [15] train-rmse:0.622047 test-rmse:0.999340 [16] train-rmse:0.605328 test-rmse:0.988456 [17] train-rmse:0.590604 test-rmse:0.988491 [18] train-rmse:0.570590 test-rmse:0.988032 [19] train-rmse:0.562011 test-rmse:0.995340 [20] train-rmse:0.546085 test-rmse:0.994451 [1] train-rmse:9.327338 test-rmse:8.813021 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.584177 test-rmse:6.176066 [3] train-rmse:4.671837 test-rmse:4.375684 [4] train-rmse:3.343866 test-rmse:3.149759 [5] train-rmse:2.431908 test-rmse:2.388063 [6] train-rmse:1.810865 test-rmse:1.968253 [7] train-rmse:1.386580 test-rmse:1.748421 [8] train-rmse:1.105844 test-rmse:1.599082 [9] train-rmse:0.921090 test-rmse:1.553314 [10] train-rmse:0.802756 test-rmse:1.531352 [11] train-rmse:0.735133 test-rmse:1.530048 [12] train-rmse:0.677135 test-rmse:1.522622 [13] train-rmse:0.627428 test-rmse:1.514477 [14] train-rmse:0.598425 test-rmse:1.529247 [15] train-rmse:0.570806 test-rmse:1.529032 [16] train-rmse:0.548069 test-rmse:1.527705 Stopping. Best iteration: [13] train-rmse:0.627428 test-rmse:1.514477
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.711580 test-rmse:8.363815 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.114823 test-rmse:5.782295 [3] train-rmse:4.301817 test-rmse:3.992208 [4] train-rmse:3.038244 test-rmse:2.780060 [5] train-rmse:2.159998 test-rmse:1.973483 [6] train-rmse:1.551210 test-rmse:1.466733 [7] train-rmse:1.131396 test-rmse:1.169153 [8] train-rmse:0.849237 test-rmse:1.023964 [9] train-rmse:0.657238 test-rmse:0.972162 [10] train-rmse:0.532531 test-rmse:0.950572 [11] train-rmse:0.460324 test-rmse:0.948392 [12] train-rmse:0.412003 test-rmse:0.955161 [13] train-rmse:0.384682 test-rmse:0.962618 [14] train-rmse:0.366186 test-rmse:0.971298 Stopping. Best iteration: [11] train-rmse:0.460324 test-rmse:0.948392 [1] train-rmse:8.640803 test-rmse:8.827027 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.065664 test-rmse:6.256505 [3] train-rmse:4.267974 test-rmse:4.464965 [4] train-rmse:3.017172 test-rmse:3.222034 [5] train-rmse:2.149648 test-rmse:2.361673 [6] train-rmse:1.550250 test-rmse:1.760617 [7] train-rmse:1.143101 test-rmse:1.351073 [8] train-rmse:0.866133 test-rmse:1.075201 [9] train-rmse:0.683201 test-rmse:0.903875 [10] train-rmse:0.571763 test-rmse:0.790646 [11] train-rmse:0.497896 test-rmse:0.730264 [12] train-rmse:0.452022 test-rmse:0.697624 [13] train-rmse:0.425795 test-rmse:0.681848 [14] train-rmse:0.404459 test-rmse:0.665380 [15] train-rmse:0.389160 test-rmse:0.661197 [16] train-rmse:0.378753 test-rmse:0.657916 [17] train-rmse:0.369889 test-rmse:0.653859 [18] train-rmse:0.366528 test-rmse:0.653287 [19] train-rmse:0.358146 test-rmse:0.663550 [20] train-rmse:0.353090 test-rmse:0.663341 [1] train-rmse:8.646050 test-rmse:8.806735 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.070551 test-rmse:6.229820 [3] train-rmse:4.271754 test-rmse:4.419957 [4] train-rmse:3.018053 test-rmse:3.167682 [5] train-rmse:2.147118 test-rmse:2.294914 [6] train-rmse:1.545914 test-rmse:1.692541 [7] train-rmse:1.133774 test-rmse:1.303355 [8] train-rmse:0.853924 test-rmse:1.062129 [9] train-rmse:0.669916 test-rmse:0.909780 [10] train-rmse:0.549855 test-rmse:0.807585 [11] train-rmse:0.476322 test-rmse:0.752046 [12] train-rmse:0.430247 test-rmse:0.731929 [13] train-rmse:0.403473 test-rmse:0.713308 [14] train-rmse:0.389299 test-rmse:0.695959 [15] train-rmse:0.378529 test-rmse:0.687881 [16] train-rmse:0.372465 test-rmse:0.682177 [17] train-rmse:0.370008 test-rmse:0.678848 [18] train-rmse:0.363855 test-rmse:0.676145 [19] train-rmse:0.360124 test-rmse:0.673170 [20] train-rmse:0.354640 test-rmse:0.675081 [1] train-rmse:8.654595 test-rmse:8.745791 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.074770 test-rmse:6.169296 [3] train-rmse:4.273568 test-rmse:4.372638 [4] train-rmse:3.019984 test-rmse:3.125094 [5] train-rmse:2.149606 test-rmse:2.265499 [6] train-rmse:1.549970 test-rmse:1.674521 [7] train-rmse:1.139593 test-rmse:1.243312 [8] train-rmse:0.862223 test-rmse:0.973471 [9] train-rmse:0.682271 test-rmse:0.799461 [10] train-rmse:0.564268 test-rmse:0.697552 [11] train-rmse:0.488185 test-rmse:0.633829 [12] train-rmse:0.439687 test-rmse:0.607435 [13] train-rmse:0.414126 test-rmse:0.598319 [14] train-rmse:0.397435 test-rmse:0.595315 [15] train-rmse:0.382041 test-rmse:0.598068 [16] train-rmse:0.375403 test-rmse:0.597525 [17] train-rmse:0.368270 test-rmse:0.600790 Stopping. Best iteration: [14] train-rmse:0.397435 test-rmse:0.595315 [1] train-rmse:8.688326 test-rmse:8.528812 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.100250 test-rmse:5.946250 [3] train-rmse:4.294040 test-rmse:4.148230 [4] train-rmse:3.036149 test-rmse:2.916992 [5] train-rmse:2.162230 test-rmse:2.085425 [6] train-rmse:1.558144 test-rmse:1.521341 [7] train-rmse:1.139903 test-rmse:1.182836 [8] train-rmse:0.857648 test-rmse:0.974665 [9] train-rmse:0.671622 test-rmse:0.855134 [10] train-rmse:0.554609 test-rmse:0.796128 [11] train-rmse:0.472651 test-rmse:0.764518 [12] train-rmse:0.424893 test-rmse:0.753426 [13] train-rmse:0.396857 test-rmse:0.749540 [14] train-rmse:0.379814 test-rmse:0.749467 [15] train-rmse:0.371074 test-rmse:0.749776 [16] train-rmse:0.363061 test-rmse:0.751395 [17] train-rmse:0.358256 test-rmse:0.751122 Stopping. Best iteration: [14] train-rmse:0.379814 test-rmse:0.749467
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.757943 test-rmse:8.699812 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.144021 test-rmse:6.098456 [3] train-rmse:4.318181 test-rmse:4.290560 [4] train-rmse:3.046134 test-rmse:3.043893 [5] train-rmse:2.164487 test-rmse:2.197340 [6] train-rmse:1.558923 test-rmse:1.641299 [7] train-rmse:1.149184 test-rmse:1.291578 [8] train-rmse:0.873343 test-rmse:1.094324 [9] train-rmse:0.696810 test-rmse:0.979266 [10] train-rmse:0.584523 test-rmse:0.924213 [11] train-rmse:0.511025 test-rmse:0.897129 [12] train-rmse:0.470587 test-rmse:0.885452 [13] train-rmse:0.442529 test-rmse:0.881725 [14] train-rmse:0.425031 test-rmse:0.875680 [15] train-rmse:0.416181 test-rmse:0.877977 [16] train-rmse:0.410905 test-rmse:0.879006 [17] train-rmse:0.402123 test-rmse:0.877494 Stopping. Best iteration: [14] train-rmse:0.425031 test-rmse:0.875680 [1] train-rmse:8.749935 test-rmse:8.755291 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.139223 test-rmse:6.153017 [3] train-rmse:4.315973 test-rmse:4.341690 [4] train-rmse:3.046217 test-rmse:3.088569 [5] train-rmse:2.166817 test-rmse:2.231806 [6] train-rmse:1.563754 test-rmse:1.654685 [7] train-rmse:1.154840 test-rmse:1.274417 [8] train-rmse:0.886321 test-rmse:1.046588 [9] train-rmse:0.709790 test-rmse:0.920180 [10] train-rmse:0.602783 test-rmse:0.855937 [11] train-rmse:0.533568 test-rmse:0.809681 [12] train-rmse:0.494068 test-rmse:0.795613 [13] train-rmse:0.470484 test-rmse:0.785801 [14] train-rmse:0.459677 test-rmse:0.780336 [15] train-rmse:0.445131 test-rmse:0.785521 [16] train-rmse:0.440751 test-rmse:0.792618 [17] train-rmse:0.434714 test-rmse:0.795692 Stopping. Best iteration: [14] train-rmse:0.459677 test-rmse:0.780336 [1] train-rmse:8.733138 test-rmse:8.868910 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.127738 test-rmse:6.264448 [3] train-rmse:4.308324 test-rmse:4.446187 [4] train-rmse:3.041416 test-rmse:3.180558 [5] train-rmse:2.164030 test-rmse:2.309771 [6] train-rmse:1.562513 test-rmse:1.708147 [7] train-rmse:1.155306 test-rmse:1.307845 [8] train-rmse:0.886592 test-rmse:1.036001 [9] train-rmse:0.711056 test-rmse:0.868833 [10] train-rmse:0.599885 test-rmse:0.777629 [11] train-rmse:0.531676 test-rmse:0.721120 [12] train-rmse:0.490566 test-rmse:0.688185 [13] train-rmse:0.466194 test-rmse:0.668830 [14] train-rmse:0.447636 test-rmse:0.658565 [15] train-rmse:0.437894 test-rmse:0.652001 [16] train-rmse:0.427722 test-rmse:0.650249 [17] train-rmse:0.422723 test-rmse:0.649296 [18] train-rmse:0.420510 test-rmse:0.648417 [19] train-rmse:0.411812 test-rmse:0.648263 [20] train-rmse:0.407449 test-rmse:0.649142 [1] train-rmse:8.759304 test-rmse:8.688820 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.145145 test-rmse:6.088673 [3] train-rmse:4.319214 test-rmse:4.282774 [4] train-rmse:3.047206 test-rmse:3.039128 [5] train-rmse:2.165725 test-rmse:2.196879 [6] train-rmse:1.560664 test-rmse:1.648206 [7] train-rmse:1.149403 test-rmse:1.290397 [8] train-rmse:0.874588 test-rmse:1.084846 [9] train-rmse:0.695602 test-rmse:0.984219 [10] train-rmse:0.584749 test-rmse:0.938670 [11] train-rmse:0.513304 test-rmse:0.920767 [12] train-rmse:0.475146 test-rmse:0.917743 [13] train-rmse:0.455491 test-rmse:0.918309 [14] train-rmse:0.442445 test-rmse:0.923601 [15] train-rmse:0.436324 test-rmse:0.927155 Stopping. Best iteration: [12] train-rmse:0.475146 test-rmse:0.917743 [1] train-rmse:8.766030 test-rmse:8.648162 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.149419 test-rmse:6.043884 [3] train-rmse:4.321588 test-rmse:4.233859 [4] train-rmse:3.047994 test-rmse:2.985838 [5] train-rmse:2.165054 test-rmse:2.139018 [6] train-rmse:1.558616 test-rmse:1.583365 [7] train-rmse:1.147788 test-rmse:1.244714 [8] train-rmse:0.874046 test-rmse:1.039842 [9] train-rmse:0.694263 test-rmse:0.930783 [10] train-rmse:0.582734 test-rmse:0.881323 [11] train-rmse:0.511687 test-rmse:0.859945 [12] train-rmse:0.469435 test-rmse:0.853169 [13] train-rmse:0.440915 test-rmse:0.846239 [14] train-rmse:0.424555 test-rmse:0.845682 [15] train-rmse:0.417859 test-rmse:0.847091 [16] train-rmse:0.409643 test-rmse:0.849066 [17] train-rmse:0.407639 test-rmse:0.850461 Stopping. Best iteration: [14] train-rmse:0.424555 test-rmse:0.845682
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.305588 test-rmse:9.400786 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.544202 test-rmse:6.640206 [3] train-rmse:4.619424 test-rmse:4.690858 [4] train-rmse:3.280991 test-rmse:3.346360 [5] train-rmse:2.356365 test-rmse:2.433236 [6] train-rmse:1.723879 test-rmse:1.834595 [7] train-rmse:1.295620 test-rmse:1.438500 [8] train-rmse:1.017983 test-rmse:1.181927 [9] train-rmse:0.843525 test-rmse:1.051238 [10] train-rmse:0.737294 test-rmse:0.981579 [11] train-rmse:0.673923 test-rmse:0.946793 [12] train-rmse:0.635397 test-rmse:0.935930 [13] train-rmse:0.604160 test-rmse:0.922804 [14] train-rmse:0.586311 test-rmse:0.922298 [15] train-rmse:0.572739 test-rmse:0.929550 [16] train-rmse:0.560683 test-rmse:0.924762 [17] train-rmse:0.549916 test-rmse:0.923153 Stopping. Best iteration: [14] train-rmse:0.586311 test-rmse:0.922298 [1] train-rmse:9.308212 test-rmse:9.370200 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.545988 test-rmse:6.625093 [3] train-rmse:4.620206 test-rmse:4.713100 [4] train-rmse:3.283116 test-rmse:3.401912 [5] train-rmse:2.360279 test-rmse:2.539621 [6] train-rmse:1.730323 test-rmse:1.937086 [7] train-rmse:1.304227 test-rmse:1.546643 [8] train-rmse:1.019988 test-rmse:1.317801 [9] train-rmse:0.843415 test-rmse:1.190621 [10] train-rmse:0.738424 test-rmse:1.104688 [11] train-rmse:0.677967 test-rmse:1.068023 [12] train-rmse:0.638575 test-rmse:1.035102 [13] train-rmse:0.614547 test-rmse:1.025246 [14] train-rmse:0.597893 test-rmse:1.018494 [15] train-rmse:0.588102 test-rmse:1.011159 [16] train-rmse:0.582277 test-rmse:1.008448 [17] train-rmse:0.577769 test-rmse:1.007053 [18] train-rmse:0.574319 test-rmse:1.004528 [19] train-rmse:0.569368 test-rmse:0.999576 [20] train-rmse:0.560095 test-rmse:1.002811 [1] train-rmse:9.358874 test-rmse:9.001379 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.580398 test-rmse:6.226299 [3] train-rmse:4.643775 test-rmse:4.299111 [4] train-rmse:3.298843 test-rmse:2.971901 [5] train-rmse:2.372633 test-rmse:2.093427 [6] train-rmse:1.736516 test-rmse:1.536882 [7] train-rmse:1.311086 test-rmse:1.232846 [8] train-rmse:1.034221 test-rmse:1.091398 [9] train-rmse:0.856310 test-rmse:1.045509 [10] train-rmse:0.749910 test-rmse:1.030200 [11] train-rmse:0.683616 test-rmse:1.047334 [12] train-rmse:0.649499 test-rmse:1.064231 [13] train-rmse:0.614960 test-rmse:1.073523 Stopping. Best iteration: [10] train-rmse:0.749910 test-rmse:1.030200 [1] train-rmse:9.317181 test-rmse:9.329130 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.551151 test-rmse:6.578157 [3] train-rmse:4.622371 test-rmse:4.625166 [4] train-rmse:3.282748 test-rmse:3.282162 [5] train-rmse:2.356946 test-rmse:2.389225 [6] train-rmse:1.724740 test-rmse:1.814690 [7] train-rmse:1.299344 test-rmse:1.454090 [8] train-rmse:1.016027 test-rmse:1.248450 [9] train-rmse:0.839627 test-rmse:1.132490 [10] train-rmse:0.732709 test-rmse:1.079190 [11] train-rmse:0.667471 test-rmse:1.045479 [12] train-rmse:0.629691 test-rmse:1.039751 [13] train-rmse:0.603920 test-rmse:1.031643 [14] train-rmse:0.586701 test-rmse:1.023783 [15] train-rmse:0.576850 test-rmse:1.024547 [16] train-rmse:0.569830 test-rmse:1.023903 [17] train-rmse:0.555733 test-rmse:1.025091 Stopping. Best iteration: [14] train-rmse:0.586701 test-rmse:1.023783 [1] train-rmse:9.315177 test-rmse:9.388052 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.552079 test-rmse:6.720072 [3] train-rmse:4.626318 test-rmse:4.905120 [4] train-rmse:3.288960 test-rmse:3.608739 [5] train-rmse:2.365059 test-rmse:2.705282 [6] train-rmse:1.733976 test-rmse:2.099116 [7] train-rmse:1.309686 test-rmse:1.668330 [8] train-rmse:1.033552 test-rmse:1.409194 [9] train-rmse:0.858066 test-rmse:1.242903 [10] train-rmse:0.748514 test-rmse:1.110646 [11] train-rmse:0.687728 test-rmse:0.999745 [12] train-rmse:0.647039 test-rmse:0.938266 [13] train-rmse:0.623048 test-rmse:0.901743 [14] train-rmse:0.600015 test-rmse:0.881604 [15] train-rmse:0.585506 test-rmse:0.863823 [16] train-rmse:0.579009 test-rmse:0.856098 [17] train-rmse:0.574876 test-rmse:0.848637 [18] train-rmse:0.567130 test-rmse:0.841614 [19] train-rmse:0.561282 test-rmse:0.840593 [20] train-rmse:0.558046 test-rmse:0.839438
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.677403 test-rmse:8.679508 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.095828 test-rmse:6.034132 [3] train-rmse:4.290939 test-rmse:4.205434 [4] train-rmse:3.032253 test-rmse:2.917098 [5] train-rmse:2.158059 test-rmse:2.053475 [6] train-rmse:1.554235 test-rmse:1.516560 [7] train-rmse:1.143772 test-rmse:1.124563 [8] train-rmse:0.867816 test-rmse:0.855199 [9] train-rmse:0.690771 test-rmse:0.696554 [10] train-rmse:0.580545 test-rmse:0.620100 [11] train-rmse:0.518097 test-rmse:0.580921 [12] train-rmse:0.480090 test-rmse:0.561862 [13] train-rmse:0.460017 test-rmse:0.559656 [14] train-rmse:0.447915 test-rmse:0.554065 [15] train-rmse:0.441346 test-rmse:0.551383 [16] train-rmse:0.437762 test-rmse:0.552102 [17] train-rmse:0.435651 test-rmse:0.553689 [18] train-rmse:0.432523 test-rmse:0.555192 Stopping. Best iteration: [15] train-rmse:0.441346 test-rmse:0.551383 [1] train-rmse:8.601503 test-rmse:9.119301 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.041714 test-rmse:6.532770 [3] train-rmse:4.252843 test-rmse:4.707654 [4] train-rmse:3.005329 test-rmse:3.435694 [5] train-rmse:2.137057 test-rmse:2.523080 [6] train-rmse:1.536332 test-rmse:1.902478 [7] train-rmse:1.126339 test-rmse:1.468401 [8] train-rmse:0.853789 test-rmse:1.210343 [9] train-rmse:0.676813 test-rmse:1.049778 [10] train-rmse:0.567561 test-rmse:1.017972 [11] train-rmse:0.503970 test-rmse:0.948041 [12] train-rmse:0.466368 test-rmse:0.925723 [13] train-rmse:0.446671 test-rmse:0.852903 [14] train-rmse:0.435817 test-rmse:0.801701 [15] train-rmse:0.429879 test-rmse:0.789707 [16] train-rmse:0.426669 test-rmse:0.803619 [17] train-rmse:0.421629 test-rmse:0.815915 [18] train-rmse:0.420373 test-rmse:0.812534 Stopping. Best iteration: [15] train-rmse:0.429879 test-rmse:0.789707 [1] train-rmse:8.691648 test-rmse:8.580938 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.104334 test-rmse:5.972338 [3] train-rmse:4.295545 test-rmse:4.188503 [4] train-rmse:3.034119 test-rmse:2.955423 [5] train-rmse:2.156249 test-rmse:2.093757 [6] train-rmse:1.549200 test-rmse:1.514784 [7] train-rmse:1.137092 test-rmse:1.173312 [8] train-rmse:0.859338 test-rmse:0.957900 [9] train-rmse:0.681065 test-rmse:0.825432 [10] train-rmse:0.569795 test-rmse:0.746200 [11] train-rmse:0.505245 test-rmse:0.712587 [12] train-rmse:0.469193 test-rmse:0.695551 [13] train-rmse:0.448986 test-rmse:0.681011 [14] train-rmse:0.437559 test-rmse:0.682274 [15] train-rmse:0.432147 test-rmse:0.685500 [16] train-rmse:0.428047 test-rmse:0.689766 Stopping. Best iteration: [13] train-rmse:0.448986 test-rmse:0.681011 [1] train-rmse:8.746120 test-rmse:8.248809 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.144679 test-rmse:5.657545 [3] train-rmse:4.326432 test-rmse:3.949513 [4] train-rmse:3.058415 test-rmse:2.770584 [5] train-rmse:2.176389 test-rmse:1.955487 [6] train-rmse:1.568253 test-rmse:1.366528 [7] train-rmse:1.151928 test-rmse:0.961995 [8] train-rmse:0.875049 test-rmse:0.724262 [9] train-rmse:0.698952 test-rmse:0.583450 [10] train-rmse:0.590082 test-rmse:0.511808 [11] train-rmse:0.525939 test-rmse:0.491243 [12] train-rmse:0.489608 test-rmse:0.490171 [13] train-rmse:0.470993 test-rmse:0.501570 [14] train-rmse:0.459985 test-rmse:0.511404 [15] train-rmse:0.452886 test-rmse:0.517111 Stopping. Best iteration: [12] train-rmse:0.489608 test-rmse:0.490171 [1] train-rmse:8.659225 test-rmse:8.781620 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.080208 test-rmse:6.236629 [3] train-rmse:4.278391 test-rmse:4.471185 [4] train-rmse:3.023239 test-rmse:3.249868 [5] train-rmse:2.152165 test-rmse:2.366054 [6] train-rmse:1.550406 test-rmse:1.761899 [7] train-rmse:1.142416 test-rmse:1.378514 [8] train-rmse:0.868177 test-rmse:1.131448 [9] train-rmse:0.693954 test-rmse:0.971460 [10] train-rmse:0.583893 test-rmse:0.879479 [11] train-rmse:0.520220 test-rmse:0.821654 [12] train-rmse:0.485025 test-rmse:0.787430 [13] train-rmse:0.464002 test-rmse:0.772324 [14] train-rmse:0.453476 test-rmse:0.764415 [15] train-rmse:0.443900 test-rmse:0.761832 [16] train-rmse:0.440493 test-rmse:0.760758 [17] train-rmse:0.436205 test-rmse:0.755684 [18] train-rmse:0.435134 test-rmse:0.756301 [19] train-rmse:0.433768 test-rmse:0.754540 [20] train-rmse:0.432791 test-rmse:0.753497
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.207086 test-rmse:9.011897 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.465491 test-rmse:6.286522 [3] train-rmse:4.551596 test-rmse:4.427179 [4] train-rmse:3.219681 test-rmse:3.136780 [5] train-rmse:2.297251 test-rmse:2.266330 [6] train-rmse:1.663427 test-rmse:1.699315 [7] train-rmse:1.233342 test-rmse:1.335218 [8] train-rmse:0.950920 test-rmse:1.127153 [9] train-rmse:0.773514 test-rmse:1.000475 [10] train-rmse:0.664150 test-rmse:0.926656 [11] train-rmse:0.603365 test-rmse:0.885939 [12] train-rmse:0.569045 test-rmse:0.870912 [13] train-rmse:0.551085 test-rmse:0.859824 [14] train-rmse:0.540818 test-rmse:0.856016 [15] train-rmse:0.535560 test-rmse:0.856997 [16] train-rmse:0.530582 test-rmse:0.859092 [17] train-rmse:0.528361 test-rmse:0.859640 Stopping. Best iteration: [14] train-rmse:0.540818 test-rmse:0.856016 [1] train-rmse:9.159916 test-rmse:9.277818 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.431547 test-rmse:6.520177 [3] train-rmse:4.527230 test-rmse:4.599418 [4] train-rmse:3.202162 test-rmse:3.267817 [5] train-rmse:2.284719 test-rmse:2.386282 [6] train-rmse:1.654893 test-rmse:1.757279 [7] train-rmse:1.230213 test-rmse:1.332391 [8] train-rmse:0.948389 test-rmse:1.086622 [9] train-rmse:0.772117 test-rmse:0.964648 [10] train-rmse:0.667650 test-rmse:0.897639 [11] train-rmse:0.609104 test-rmse:0.868960 [12] train-rmse:0.572289 test-rmse:0.855980 [13] train-rmse:0.554740 test-rmse:0.852861 [14] train-rmse:0.543671 test-rmse:0.870154 [15] train-rmse:0.537461 test-rmse:0.875264 [16] train-rmse:0.533986 test-rmse:0.878486 Stopping. Best iteration: [13] train-rmse:0.554740 test-rmse:0.852861 [1] train-rmse:9.172465 test-rmse:9.159298 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.440509 test-rmse:6.387027 [3] train-rmse:4.533268 test-rmse:4.484226 [4] train-rmse:3.205596 test-rmse:3.193618 [5] train-rmse:2.286440 test-rmse:2.313053 [6] train-rmse:1.655901 test-rmse:1.700258 [7] train-rmse:1.230939 test-rmse:1.324965 [8] train-rmse:0.952800 test-rmse:1.104545 [9] train-rmse:0.778654 test-rmse:0.971826 [10] train-rmse:0.674574 test-rmse:0.898730 [11] train-rmse:0.615891 test-rmse:0.863794 [12] train-rmse:0.582594 test-rmse:0.842893 [13] train-rmse:0.563369 test-rmse:0.822871 [14] train-rmse:0.554032 test-rmse:0.819891 [15] train-rmse:0.547135 test-rmse:0.817878 [16] train-rmse:0.543065 test-rmse:0.814943 [17] train-rmse:0.538432 test-rmse:0.811236 [18] train-rmse:0.536792 test-rmse:0.809493 [19] train-rmse:0.535770 test-rmse:0.808815 [20] train-rmse:0.532328 test-rmse:0.809433 [1] train-rmse:9.194613 test-rmse:9.104988 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.456209 test-rmse:6.405414 [3] train-rmse:4.545327 test-rmse:4.549923 [4] train-rmse:3.215573 test-rmse:3.239730 [5] train-rmse:2.295695 test-rmse:2.353886 [6] train-rmse:1.663199 test-rmse:1.741138 [7] train-rmse:1.237090 test-rmse:1.352575 [8] train-rmse:0.956024 test-rmse:1.106751 [9] train-rmse:0.780019 test-rmse:0.959811 [10] train-rmse:0.674475 test-rmse:0.891323 [11] train-rmse:0.615671 test-rmse:0.853039 [12] train-rmse:0.582145 test-rmse:0.840148 [13] train-rmse:0.563561 test-rmse:0.838661 [14] train-rmse:0.553594 test-rmse:0.836087 [15] train-rmse:0.547298 test-rmse:0.839481 [16] train-rmse:0.543841 test-rmse:0.838369 [17] train-rmse:0.541088 test-rmse:0.844372 Stopping. Best iteration: [14] train-rmse:0.553594 test-rmse:0.836087 [1] train-rmse:9.172386 test-rmse:9.255140 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.441896 test-rmse:6.558523 [3] train-rmse:4.537215 test-rmse:4.670046 [4] train-rmse:3.211501 test-rmse:3.364163 [5] train-rmse:2.294503 test-rmse:2.469015 [6] train-rmse:1.664661 test-rmse:2.106003 [7] train-rmse:1.241710 test-rmse:1.691773 [8] train-rmse:0.963090 test-rmse:1.419047 [9] train-rmse:0.788637 test-rmse:1.233050 [10] train-rmse:0.683608 test-rmse:1.108963 [11] train-rmse:0.622584 test-rmse:1.024684 [12] train-rmse:0.590568 test-rmse:0.978461 [13] train-rmse:0.572333 test-rmse:0.943944 [14] train-rmse:0.562515 test-rmse:0.898847 [15] train-rmse:0.555994 test-rmse:0.886457 [16] train-rmse:0.553365 test-rmse:0.902894 [17] train-rmse:0.550597 test-rmse:0.903084 [18] train-rmse:0.547882 test-rmse:0.897897 Stopping. Best iteration: [15] train-rmse:0.555994 test-rmse:0.886457
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.508665 test-rmse:9.731531 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.706135 test-rmse:6.951339 [3] train-rmse:4.759617 test-rmse:5.013197 [4] train-rmse:3.412184 test-rmse:3.725281 [5] train-rmse:2.489660 test-rmse:2.795808 [6] train-rmse:1.863822 test-rmse:2.207271 [7] train-rmse:1.451990 test-rmse:1.811273 [8] train-rmse:1.185474 test-rmse:1.562864 [9] train-rmse:1.023666 test-rmse:1.410059 [10] train-rmse:0.915720 test-rmse:1.291994 [11] train-rmse:0.859772 test-rmse:1.248062 [12] train-rmse:0.815184 test-rmse:1.222323 [13] train-rmse:0.789501 test-rmse:1.196357 [14] train-rmse:0.777837 test-rmse:1.188355 [15] train-rmse:0.756163 test-rmse:1.184086 [16] train-rmse:0.745075 test-rmse:1.180642 [17] train-rmse:0.738696 test-rmse:1.171801 [18] train-rmse:0.730986 test-rmse:1.171673 [19] train-rmse:0.710363 test-rmse:1.177899 [20] train-rmse:0.691079 test-rmse:1.179509 [1] train-rmse:9.542632 test-rmse:9.501602 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.728758 test-rmse:6.689415 [3] train-rmse:4.774064 test-rmse:4.734704 [4] train-rmse:3.425067 test-rmse:3.396367 [5] train-rmse:2.496257 test-rmse:2.548566 [6] train-rmse:1.862226 test-rmse:1.937904 [7] train-rmse:1.444039 test-rmse:1.590081 [8] train-rmse:1.180604 test-rmse:1.372078 [9] train-rmse:1.019641 test-rmse:1.260368 [10] train-rmse:0.906906 test-rmse:1.216744 [11] train-rmse:0.846004 test-rmse:1.192245 [12] train-rmse:0.796243 test-rmse:1.180587 [13] train-rmse:0.769329 test-rmse:1.178825 [14] train-rmse:0.744372 test-rmse:1.182618 [15] train-rmse:0.724036 test-rmse:1.202378 [16] train-rmse:0.713364 test-rmse:1.203829 Stopping. Best iteration: [13] train-rmse:0.769329 test-rmse:1.178825 [1] train-rmse:9.607680 test-rmse:9.186364 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.772362 test-rmse:6.396841 [3] train-rmse:4.800287 test-rmse:4.505610 [4] train-rmse:3.438562 test-rmse:3.255769 [5] train-rmse:2.506548 test-rmse:2.444216 [6] train-rmse:1.873536 test-rmse:1.982113 [7] train-rmse:1.454072 test-rmse:1.750181 [8] train-rmse:1.189826 test-rmse:1.650450 [9] train-rmse:1.026073 test-rmse:1.634662 [10] train-rmse:0.928390 test-rmse:1.644825 [11] train-rmse:0.874154 test-rmse:1.668303 [12] train-rmse:0.832862 test-rmse:1.684423 Stopping. Best iteration: [9] train-rmse:1.026073 test-rmse:1.634662 [1] train-rmse:9.546103 test-rmse:9.455536 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.730135 test-rmse:6.654431 [3] train-rmse:4.773525 test-rmse:4.712755 [4] train-rmse:3.420798 test-rmse:3.404648 [5] train-rmse:2.489285 test-rmse:2.525411 [6] train-rmse:1.861788 test-rmse:1.961331 [7] train-rmse:1.447139 test-rmse:1.625809 [8] train-rmse:1.172354 test-rmse:1.435421 [9] train-rmse:1.006104 test-rmse:1.341759 [10] train-rmse:0.899426 test-rmse:1.299984 [11] train-rmse:0.833697 test-rmse:1.284977 [12] train-rmse:0.790968 test-rmse:1.285099 [13] train-rmse:0.764682 test-rmse:1.288797 [14] train-rmse:0.741637 test-rmse:1.291420 Stopping. Best iteration: [11] train-rmse:0.833697 test-rmse:1.284977 [1] train-rmse:9.470266 test-rmse:9.998213 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.676673 test-rmse:7.192227 [3] train-rmse:4.735589 test-rmse:5.251865 [4] train-rmse:3.392387 test-rmse:3.931309 [5] train-rmse:2.469102 test-rmse:3.012022 [6] train-rmse:1.841701 test-rmse:2.409984 [7] train-rmse:1.422284 test-rmse:2.017177 [8] train-rmse:1.155228 test-rmse:1.782247 [9] train-rmse:0.986902 test-rmse:1.622549 [10] train-rmse:0.884527 test-rmse:1.540596 [11] train-rmse:0.822464 test-rmse:1.490861 [12] train-rmse:0.784638 test-rmse:1.454469 [13] train-rmse:0.762971 test-rmse:1.424499 [14] train-rmse:0.750700 test-rmse:1.410362 [15] train-rmse:0.734337 test-rmse:1.395930 [16] train-rmse:0.716381 test-rmse:1.393912 [17] train-rmse:0.705477 test-rmse:1.382925 [18] train-rmse:0.694386 test-rmse:1.378722 [19] train-rmse:0.690032 test-rmse:1.374503 [20] train-rmse:0.683197 test-rmse:1.364121
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.074142 test-rmse:9.167627 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.370614 test-rmse:6.469861 [3] train-rmse:4.482539 test-rmse:4.602886 [4] train-rmse:3.167151 test-rmse:3.302197 [5] train-rmse:2.252189 test-rmse:2.418974 [6] train-rmse:1.621337 test-rmse:1.835112 [7] train-rmse:1.190191 test-rmse:1.453531 [8] train-rmse:0.898173 test-rmse:1.186265 [9] train-rmse:0.707550 test-rmse:1.058915 [10] train-rmse:0.591883 test-rmse:0.970772 [11] train-rmse:0.524351 test-rmse:0.924328 [12] train-rmse:0.485170 test-rmse:0.895087 [13] train-rmse:0.461503 test-rmse:0.883412 [14] train-rmse:0.445941 test-rmse:0.862098 [15] train-rmse:0.431213 test-rmse:0.860119 [16] train-rmse:0.424526 test-rmse:0.855905 [17] train-rmse:0.420070 test-rmse:0.854780 [18] train-rmse:0.413727 test-rmse:0.854193 [19] train-rmse:0.411233 test-rmse:0.851109 [20] train-rmse:0.401942 test-rmse:0.857487 [1] train-rmse:9.120785 test-rmse:8.859761 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.402043 test-rmse:6.154218 [3] train-rmse:4.502892 test-rmse:4.241718 [4] train-rmse:3.179233 test-rmse:2.938496 [5] train-rmse:2.258935 test-rmse:2.074587 [6] train-rmse:1.624605 test-rmse:1.520504 [7] train-rmse:1.192196 test-rmse:1.195673 [8] train-rmse:0.901888 test-rmse:1.027789 [9] train-rmse:0.714242 test-rmse:0.950756 [10] train-rmse:0.596809 test-rmse:0.921056 [11] train-rmse:0.525903 test-rmse:0.921233 [12] train-rmse:0.480599 test-rmse:0.925884 [13] train-rmse:0.456819 test-rmse:0.939789 Stopping. Best iteration: [10] train-rmse:0.596809 test-rmse:0.921056 [1] train-rmse:9.122521 test-rmse:8.899934 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.405374 test-rmse:6.206759 [3] train-rmse:4.508870 test-rmse:4.318073 [4] train-rmse:3.187192 test-rmse:3.013927 [5] train-rmse:2.268458 test-rmse:2.111617 [6] train-rmse:1.632640 test-rmse:1.501398 [7] train-rmse:1.200496 test-rmse:1.115868 [8] train-rmse:0.908021 test-rmse:0.884808 [9] train-rmse:0.720797 test-rmse:0.779334 [10] train-rmse:0.599228 test-rmse:0.725180 [11] train-rmse:0.528724 test-rmse:0.711826 [12] train-rmse:0.480103 test-rmse:0.704454 [13] train-rmse:0.455633 test-rmse:0.714470 [14] train-rmse:0.440603 test-rmse:0.721530 [15] train-rmse:0.426254 test-rmse:0.727160 Stopping. Best iteration: [12] train-rmse:0.480103 test-rmse:0.704454 [1] train-rmse:9.060252 test-rmse:9.254415 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.363392 test-rmse:6.529369 [3] train-rmse:4.482027 test-rmse:4.623081 [4] train-rmse:3.169607 test-rmse:3.327172 [5] train-rmse:2.259699 test-rmse:2.420590 [6] train-rmse:1.632660 test-rmse:1.788296 [7] train-rmse:1.206160 test-rmse:1.367320 [8] train-rmse:0.916142 test-rmse:1.090492 [9] train-rmse:0.732452 test-rmse:0.899422 [10] train-rmse:0.612211 test-rmse:0.772571 [11] train-rmse:0.540727 test-rmse:0.702024 [12] train-rmse:0.502146 test-rmse:0.644706 [13] train-rmse:0.477381 test-rmse:0.609093 [14] train-rmse:0.457147 test-rmse:0.586235 [15] train-rmse:0.448139 test-rmse:0.571464 [16] train-rmse:0.439518 test-rmse:0.565399 [17] train-rmse:0.427129 test-rmse:0.569630 [18] train-rmse:0.423720 test-rmse:0.567542 [19] train-rmse:0.418817 test-rmse:0.566352 Stopping. Best iteration: [16] train-rmse:0.439518 test-rmse:0.565399 [1] train-rmse:9.075330 test-rmse:9.193081 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.371068 test-rmse:6.500402 [3] train-rmse:4.483127 test-rmse:4.614853 [4] train-rmse:3.167308 test-rmse:3.304885 [5] train-rmse:2.250612 test-rmse:2.396314 [6] train-rmse:1.620264 test-rmse:1.778888 [7] train-rmse:1.188890 test-rmse:1.363679 [8] train-rmse:0.900789 test-rmse:1.088780 [9] train-rmse:0.713780 test-rmse:0.913218 [10] train-rmse:0.596162 test-rmse:0.788873 [11] train-rmse:0.527310 test-rmse:0.724720 [12] train-rmse:0.486444 test-rmse:0.695803 [13] train-rmse:0.462745 test-rmse:0.685062 [14] train-rmse:0.449528 test-rmse:0.675302 [15] train-rmse:0.431171 test-rmse:0.669217 [16] train-rmse:0.426443 test-rmse:0.680734 [17] train-rmse:0.415056 test-rmse:0.678478 [18] train-rmse:0.411064 test-rmse:0.680184 Stopping. Best iteration: [15] train-rmse:0.431171 test-rmse:0.669217
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.115062 test-rmse:9.428316 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.402051 test-rmse:6.692321 [3] train-rmse:4.509795 test-rmse:4.782611 [4] train-rmse:3.192431 test-rmse:3.445341 [5] train-rmse:2.280827 test-rmse:2.523388 [6] train-rmse:1.656049 test-rmse:1.899729 [7] train-rmse:1.234534 test-rmse:1.463439 [8] train-rmse:0.957713 test-rmse:1.201475 [9] train-rmse:0.783058 test-rmse:1.015883 [10] train-rmse:0.674215 test-rmse:0.930301 [11] train-rmse:0.614834 test-rmse:0.881984 [12] train-rmse:0.580992 test-rmse:0.855339 [13] train-rmse:0.555821 test-rmse:0.838878 [14] train-rmse:0.536879 test-rmse:0.830996 [15] train-rmse:0.523251 test-rmse:0.829038 [16] train-rmse:0.514103 test-rmse:0.824999 [17] train-rmse:0.508623 test-rmse:0.823999 [18] train-rmse:0.506848 test-rmse:0.821897 [19] train-rmse:0.499409 test-rmse:0.824878 [20] train-rmse:0.494503 test-rmse:0.824294 [1] train-rmse:9.171201 test-rmse:9.055923 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.440594 test-rmse:6.318071 [3] train-rmse:4.535987 test-rmse:4.421376 [4] train-rmse:3.210776 test-rmse:3.144802 [5] train-rmse:2.293830 test-rmse:2.260665 [6] train-rmse:1.662646 test-rmse:1.675706 [7] train-rmse:1.236740 test-rmse:1.299827 [8] train-rmse:0.957462 test-rmse:1.073836 [9] train-rmse:0.779522 test-rmse:0.958783 [10] train-rmse:0.668439 test-rmse:0.897751 [11] train-rmse:0.604214 test-rmse:0.867381 [12] train-rmse:0.569338 test-rmse:0.855330 [13] train-rmse:0.546049 test-rmse:0.852247 [14] train-rmse:0.529502 test-rmse:0.854670 [15] train-rmse:0.517326 test-rmse:0.857659 [16] train-rmse:0.508384 test-rmse:0.847716 [17] train-rmse:0.505418 test-rmse:0.851612 [18] train-rmse:0.494055 test-rmse:0.851788 [19] train-rmse:0.489680 test-rmse:0.853916 Stopping. Best iteration: [16] train-rmse:0.508384 test-rmse:0.847716 [1] train-rmse:9.232126 test-rmse:8.670464 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.483286 test-rmse:5.903545 [3] train-rmse:4.565472 test-rmse:4.009427 [4] train-rmse:3.229655 test-rmse:2.681874 [5] train-rmse:2.306351 test-rmse:1.836002 [6] train-rmse:1.669642 test-rmse:1.313736 [7] train-rmse:1.239764 test-rmse:1.048144 [8] train-rmse:0.956983 test-rmse:0.947308 [9] train-rmse:0.772663 test-rmse:0.941382 [10] train-rmse:0.661950 test-rmse:0.960311 [11] train-rmse:0.592570 test-rmse:0.993110 [12] train-rmse:0.554006 test-rmse:1.019215 Stopping. Best iteration: [9] train-rmse:0.772663 test-rmse:0.941382 [1] train-rmse:9.178247 test-rmse:9.032164 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.447485 test-rmse:6.323424 [3] train-rmse:4.541048 test-rmse:4.399275 [4] train-rmse:3.213468 test-rmse:3.094713 [5] train-rmse:2.294409 test-rmse:2.189826 [6] train-rmse:1.664229 test-rmse:1.611428 [7] train-rmse:1.238302 test-rmse:1.253452 [8] train-rmse:0.959864 test-rmse:1.059791 [9] train-rmse:0.782245 test-rmse:0.961719 [10] train-rmse:0.677215 test-rmse:0.909395 [11] train-rmse:0.613873 test-rmse:0.895339 [12] train-rmse:0.577938 test-rmse:0.886597 [13] train-rmse:0.557471 test-rmse:0.891573 [14] train-rmse:0.544633 test-rmse:0.887320 [15] train-rmse:0.535164 test-rmse:0.888268 Stopping. Best iteration: [12] train-rmse:0.577938 test-rmse:0.886597 [1] train-rmse:9.108737 test-rmse:9.466683 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.398773 test-rmse:6.756265 [3] train-rmse:4.507490 test-rmse:4.816365 [4] train-rmse:3.193650 test-rmse:3.489141 [5] train-rmse:2.283076 test-rmse:2.562380 [6] train-rmse:1.657842 test-rmse:1.921280 [7] train-rmse:1.236880 test-rmse:1.502634 [8] train-rmse:0.962379 test-rmse:1.225185 [9] train-rmse:0.790443 test-rmse:1.042896 [10] train-rmse:0.683745 test-rmse:0.938866 [11] train-rmse:0.622110 test-rmse:0.874981 [12] train-rmse:0.581274 test-rmse:0.838259 [13] train-rmse:0.562666 test-rmse:0.818736 [14] train-rmse:0.552042 test-rmse:0.801557 [15] train-rmse:0.540768 test-rmse:0.790318 [16] train-rmse:0.527513 test-rmse:0.780126 [17] train-rmse:0.522717 test-rmse:0.773312 [18] train-rmse:0.518096 test-rmse:0.769402 [19] train-rmse:0.512618 test-rmse:0.770179 [20] train-rmse:0.505237 test-rmse:0.760686
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.685628 test-rmse:9.047315 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.094935 test-rmse:6.405883 [3] train-rmse:4.284873 test-rmse:4.534035 [4] train-rmse:3.023015 test-rmse:3.226982 [5] train-rmse:2.145334 test-rmse:2.336195 [6] train-rmse:1.536998 test-rmse:1.715304 [7] train-rmse:1.120796 test-rmse:1.291942 [8] train-rmse:0.839485 test-rmse:1.034093 [9] train-rmse:0.657651 test-rmse:0.878558 [10] train-rmse:0.541240 test-rmse:0.760771 [11] train-rmse:0.471820 test-rmse:0.675490 [12] train-rmse:0.433865 test-rmse:0.629748 [13] train-rmse:0.405404 test-rmse:0.601596 [14] train-rmse:0.389528 test-rmse:0.588830 [15] train-rmse:0.382394 test-rmse:0.577198 [16] train-rmse:0.373122 test-rmse:0.574756 [17] train-rmse:0.370165 test-rmse:0.577294 [18] train-rmse:0.368087 test-rmse:0.574824 [19] train-rmse:0.362413 test-rmse:0.574892 Stopping. Best iteration: [16] train-rmse:0.373122 test-rmse:0.574756 [1] train-rmse:8.730112 test-rmse:8.813999 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.126793 test-rmse:6.198964 [3] train-rmse:4.308450 test-rmse:4.361294 [4] train-rmse:3.040674 test-rmse:3.074079 [5] train-rmse:2.159293 test-rmse:2.194585 [6] train-rmse:1.550369 test-rmse:1.596874 [7] train-rmse:1.133515 test-rmse:1.180511 [8] train-rmse:0.854529 test-rmse:0.901115 [9] train-rmse:0.670542 test-rmse:0.718430 [10] train-rmse:0.555046 test-rmse:0.612157 [11] train-rmse:0.484938 test-rmse:0.547150 [12] train-rmse:0.448081 test-rmse:0.510322 [13] train-rmse:0.425397 test-rmse:0.491243 [14] train-rmse:0.408521 test-rmse:0.480162 [15] train-rmse:0.400976 test-rmse:0.471553 [16] train-rmse:0.389571 test-rmse:0.468679 [17] train-rmse:0.387331 test-rmse:0.468766 [18] train-rmse:0.378319 test-rmse:0.465149 [19] train-rmse:0.369915 test-rmse:0.466051 [20] train-rmse:0.365873 test-rmse:0.466513 [1] train-rmse:8.826596 test-rmse:8.362290 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.194737 test-rmse:5.833878 [3] train-rmse:4.355847 test-rmse:4.069096 [4] train-rmse:3.073443 test-rmse:2.837123 [5] train-rmse:2.180986 test-rmse:2.016831 [6] train-rmse:1.563445 test-rmse:1.449954 [7] train-rmse:1.139529 test-rmse:1.081699 [8] train-rmse:0.855106 test-rmse:0.823423 [9] train-rmse:0.669316 test-rmse:0.655081 [10] train-rmse:0.550057 test-rmse:0.557655 [11] train-rmse:0.480586 test-rmse:0.501944 [12] train-rmse:0.438183 test-rmse:0.481624 [13] train-rmse:0.411030 test-rmse:0.475122 [14] train-rmse:0.398550 test-rmse:0.468064 [15] train-rmse:0.389996 test-rmse:0.459055 [16] train-rmse:0.381700 test-rmse:0.458507 [17] train-rmse:0.371620 test-rmse:0.458727 [18] train-rmse:0.362759 test-rmse:0.460305 [19] train-rmse:0.358052 test-rmse:0.460833 Stopping. Best iteration: [16] train-rmse:0.381700 test-rmse:0.458507 [1] train-rmse:8.756870 test-rmse:8.711932 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.144706 test-rmse:6.143349 [3] train-rmse:4.319265 test-rmse:4.359163 [4] train-rmse:3.045603 test-rmse:3.111109 [5] train-rmse:2.159715 test-rmse:2.243982 [6] train-rmse:1.546563 test-rmse:1.653165 [7] train-rmse:1.127562 test-rmse:1.277147 [8] train-rmse:0.844606 test-rmse:1.026253 [9] train-rmse:0.656915 test-rmse:0.881615 [10] train-rmse:0.540001 test-rmse:0.788814 [11] train-rmse:0.469069 test-rmse:0.741700 [12] train-rmse:0.428814 test-rmse:0.715223 [13] train-rmse:0.406439 test-rmse:0.702300 [14] train-rmse:0.390254 test-rmse:0.700489 [15] train-rmse:0.376523 test-rmse:0.696146 [16] train-rmse:0.370564 test-rmse:0.694161 [17] train-rmse:0.367469 test-rmse:0.693926 [18] train-rmse:0.355559 test-rmse:0.696286 [19] train-rmse:0.347470 test-rmse:0.697358 [20] train-rmse:0.346214 test-rmse:0.697147 Stopping. Best iteration: [17] train-rmse:0.367469 test-rmse:0.693926 [1] train-rmse:8.710165 test-rmse:8.835963 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.110902 test-rmse:6.174177 [3] train-rmse:4.294974 test-rmse:4.321983 [4] train-rmse:3.028004 test-rmse:3.060805 [5] train-rmse:2.145626 test-rmse:2.177420 [6] train-rmse:1.533534 test-rmse:1.609684 [7] train-rmse:1.112015 test-rmse:1.246173 [8] train-rmse:0.827791 test-rmse:1.019628 [9] train-rmse:0.641281 test-rmse:0.896205 [10] train-rmse:0.517852 test-rmse:0.826040 [11] train-rmse:0.442046 test-rmse:0.800482 [12] train-rmse:0.399727 test-rmse:0.779262 [13] train-rmse:0.371972 test-rmse:0.766815 [14] train-rmse:0.355248 test-rmse:0.772581 [15] train-rmse:0.344323 test-rmse:0.771569 [16] train-rmse:0.338017 test-rmse:0.772932 Stopping. Best iteration: [13] train-rmse:0.371972 test-rmse:0.766815
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.420343 test-rmse:8.392656 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.906713 test-rmse:5.879511 [3] train-rmse:4.150189 test-rmse:4.133840 [4] train-rmse:2.924512 test-rmse:2.934422 [5] train-rmse:2.070925 test-rmse:2.131323 [6] train-rmse:1.480293 test-rmse:1.578456 [7] train-rmse:1.076901 test-rmse:1.216435 [8] train-rmse:0.807152 test-rmse:0.967074 [9] train-rmse:0.633193 test-rmse:0.816742 [10] train-rmse:0.525021 test-rmse:0.732593 [11] train-rmse:0.462215 test-rmse:0.688182 [12] train-rmse:0.426888 test-rmse:0.656817 [13] train-rmse:0.408287 test-rmse:0.633236 [14] train-rmse:0.398511 test-rmse:0.618032 [15] train-rmse:0.393228 test-rmse:0.609510 [16] train-rmse:0.390180 test-rmse:0.604923 [17] train-rmse:0.388612 test-rmse:0.600743 [18] train-rmse:0.387566 test-rmse:0.605431 [19] train-rmse:0.386717 test-rmse:0.605913 [20] train-rmse:0.386256 test-rmse:0.609422 Stopping. Best iteration: [17] train-rmse:0.388612 test-rmse:0.600743 [1] train-rmse:8.416825 test-rmse:8.408947 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.904216 test-rmse:5.868158 [3] train-rmse:4.149323 test-rmse:4.095599 [4] train-rmse:2.923825 test-rmse:2.831950 [5] train-rmse:2.071011 test-rmse:1.975878 [6] train-rmse:1.480759 test-rmse:1.391056 [7] train-rmse:1.076829 test-rmse:1.089617 [8] train-rmse:0.806494 test-rmse:0.823538 [9] train-rmse:0.630846 test-rmse:0.683305 [10] train-rmse:0.521676 test-rmse:0.610376 [11] train-rmse:0.457366 test-rmse:0.568535 [12] train-rmse:0.421765 test-rmse:0.552869 [13] train-rmse:0.402333 test-rmse:0.538786 [14] train-rmse:0.391937 test-rmse:0.540016 [15] train-rmse:0.386717 test-rmse:0.540109 [16] train-rmse:0.383836 test-rmse:0.541335 Stopping. Best iteration: [13] train-rmse:0.402333 test-rmse:0.538786 [1] train-rmse:8.392652 test-rmse:8.557063 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.886595 test-rmse:6.056090 [3] train-rmse:4.134608 test-rmse:4.319461 [4] train-rmse:2.912106 test-rmse:3.114283 [5] train-rmse:2.060305 test-rmse:2.256116 [6] train-rmse:1.470557 test-rmse:1.675501 [7] train-rmse:1.066428 test-rmse:1.281609 [8] train-rmse:0.793872 test-rmse:1.025811 [9] train-rmse:0.616536 test-rmse:0.861283 [10] train-rmse:0.504969 test-rmse:0.752421 [11] train-rmse:0.438038 test-rmse:0.694441 [12] train-rmse:0.400564 test-rmse:0.646884 [13] train-rmse:0.379836 test-rmse:0.627454 [14] train-rmse:0.368869 test-rmse:0.608016 [15] train-rmse:0.363026 test-rmse:0.596927 [16] train-rmse:0.359364 test-rmse:0.591962 [17] train-rmse:0.357780 test-rmse:0.585289 [18] train-rmse:0.356809 test-rmse:0.581654 [19] train-rmse:0.356146 test-rmse:0.581554 [20] train-rmse:0.355351 test-rmse:0.580342 [1] train-rmse:8.429543 test-rmse:8.316669 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.912934 test-rmse:5.851369 [3] train-rmse:4.153974 test-rmse:4.128495 [4] train-rmse:2.925984 test-rmse:2.895431 [5] train-rmse:2.070975 test-rmse:2.066104 [6] train-rmse:1.479008 test-rmse:1.527477 [7] train-rmse:1.072401 test-rmse:1.063731 [8] train-rmse:0.798829 test-rmse:0.775773 [9] train-rmse:0.621270 test-rmse:0.625640 [10] train-rmse:0.510470 test-rmse:0.571221 [11] train-rmse:0.445326 test-rmse:0.572131 [12] train-rmse:0.409302 test-rmse:0.576300 [13] train-rmse:0.389064 test-rmse:0.588623 Stopping. Best iteration: [10] train-rmse:0.510470 test-rmse:0.571221 [1] train-rmse:8.411026 test-rmse:8.449055 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.900709 test-rmse:5.915004 [3] train-rmse:4.146686 test-rmse:4.157461 [4] train-rmse:2.922619 test-rmse:2.908334 [5] train-rmse:2.071634 test-rmse:2.057534 [6] train-rmse:1.482391 test-rmse:1.500719 [7] train-rmse:1.079105 test-rmse:1.093506 [8] train-rmse:0.808815 test-rmse:0.851126 [9] train-rmse:0.633596 test-rmse:0.687823 [10] train-rmse:0.525368 test-rmse:0.599239 [11] train-rmse:0.461873 test-rmse:0.540470 [12] train-rmse:0.426168 test-rmse:0.500238 [13] train-rmse:0.407528 test-rmse:0.483578 [14] train-rmse:0.397623 test-rmse:0.474529 [15] train-rmse:0.392150 test-rmse:0.470473 [16] train-rmse:0.389248 test-rmse:0.466670 [17] train-rmse:0.387708 test-rmse:0.464739 [18] train-rmse:0.386944 test-rmse:0.464160 [19] train-rmse:0.386330 test-rmse:0.466880 [20] train-rmse:0.385853 test-rmse:0.469349
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.908722 test-rmse:8.838648 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.254673 test-rmse:6.215365 [3] train-rmse:4.401845 test-rmse:4.371818 [4] train-rmse:3.111961 test-rmse:3.090410 [5] train-rmse:2.214521 test-rmse:2.215822 [6] train-rmse:1.597374 test-rmse:1.624010 [7] train-rmse:1.178507 test-rmse:1.255084 [8] train-rmse:0.901538 test-rmse:1.018319 [9] train-rmse:0.727141 test-rmse:0.882437 [10] train-rmse:0.622142 test-rmse:0.811139 [11] train-rmse:0.561983 test-rmse:0.762782 [12] train-rmse:0.529874 test-rmse:0.743126 [13] train-rmse:0.512251 test-rmse:0.733388 [14] train-rmse:0.501392 test-rmse:0.725322 [15] train-rmse:0.494798 test-rmse:0.727828 [16] train-rmse:0.490649 test-rmse:0.726863 [17] train-rmse:0.488924 test-rmse:0.728665 Stopping. Best iteration: [14] train-rmse:0.501392 test-rmse:0.725322 [1] train-rmse:8.877645 test-rmse:8.949286 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.234888 test-rmse:6.261649 [3] train-rmse:4.388529 test-rmse:4.399728 [4] train-rmse:3.103337 test-rmse:3.081625 [5] train-rmse:2.210017 test-rmse:2.130855 [6] train-rmse:1.595313 test-rmse:1.476675 [7] train-rmse:1.178493 test-rmse:1.040163 [8] train-rmse:0.903451 test-rmse:0.769220 [9] train-rmse:0.729514 test-rmse:0.625164 [10] train-rmse:0.625877 test-rmse:0.563409 [11] train-rmse:0.566249 test-rmse:0.549278 [12] train-rmse:0.533142 test-rmse:0.554092 [13] train-rmse:0.514895 test-rmse:0.563778 [14] train-rmse:0.505711 test-rmse:0.573274 Stopping. Best iteration: [11] train-rmse:0.566249 test-rmse:0.549278 [1] train-rmse:8.831444 test-rmse:9.273446 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.197089 test-rmse:6.579984 [3] train-rmse:4.355264 test-rmse:4.794871 [4] train-rmse:3.071320 test-rmse:3.576142 [5] train-rmse:2.178631 test-rmse:2.780666 [6] train-rmse:1.560984 test-rmse:2.258410 [7] train-rmse:1.138830 test-rmse:1.901345 [8] train-rmse:0.855622 test-rmse:1.701130 [9] train-rmse:0.672036 test-rmse:1.572539 [10] train-rmse:0.558669 test-rmse:1.504137 [11] train-rmse:0.491849 test-rmse:1.455305 [12] train-rmse:0.454503 test-rmse:1.424809 [13] train-rmse:0.433192 test-rmse:1.401367 [14] train-rmse:0.421074 test-rmse:1.385547 [15] train-rmse:0.412849 test-rmse:1.377709 [16] train-rmse:0.409203 test-rmse:1.370062 [17] train-rmse:0.406327 test-rmse:1.365887 [18] train-rmse:0.404341 test-rmse:1.362485 [19] train-rmse:0.402764 test-rmse:1.329352 [20] train-rmse:0.401629 test-rmse:1.326875 [1] train-rmse:8.944870 test-rmse:8.649231 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.280500 test-rmse:6.072122 [3] train-rmse:4.420455 test-rmse:4.225555 [4] train-rmse:3.125329 test-rmse:2.979932 [5] train-rmse:2.223357 test-rmse:2.146935 [6] train-rmse:1.602741 test-rmse:1.569135 [7] train-rmse:1.184283 test-rmse:1.241763 [8] train-rmse:0.906304 test-rmse:1.027574 [9] train-rmse:0.730026 test-rmse:0.873338 [10] train-rmse:0.624190 test-rmse:0.800860 [11] train-rmse:0.564862 test-rmse:0.761244 [12] train-rmse:0.532746 test-rmse:0.740638 [13] train-rmse:0.514824 test-rmse:0.730437 [14] train-rmse:0.503457 test-rmse:0.717599 [15] train-rmse:0.498293 test-rmse:0.714812 [16] train-rmse:0.494978 test-rmse:0.711794 [17] train-rmse:0.493148 test-rmse:0.724373 [18] train-rmse:0.491971 test-rmse:0.722505 [19] train-rmse:0.491270 test-rmse:0.734011 Stopping. Best iteration: [16] train-rmse:0.494978 test-rmse:0.711794 [1] train-rmse:8.918303 test-rmse:8.744721 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.260246 test-rmse:6.145892 [3] train-rmse:4.403994 test-rmse:4.333863 [4] train-rmse:3.111395 test-rmse:3.083482 [5] train-rmse:2.215568 test-rmse:2.234375 [6] train-rmse:1.596954 test-rmse:1.645664 [7] train-rmse:1.178611 test-rmse:1.272723 [8] train-rmse:0.902273 test-rmse:1.021425 [9] train-rmse:0.728388 test-rmse:0.884715 [10] train-rmse:0.620762 test-rmse:0.809661 [11] train-rmse:0.561134 test-rmse:0.777711 [12] train-rmse:0.525439 test-rmse:0.753473 [13] train-rmse:0.506567 test-rmse:0.742528 [14] train-rmse:0.495935 test-rmse:0.742444 [15] train-rmse:0.490337 test-rmse:0.739190 [16] train-rmse:0.487144 test-rmse:0.743599 [17] train-rmse:0.485054 test-rmse:0.740500 [18] train-rmse:0.483905 test-rmse:0.742841 Stopping. Best iteration: [15] train-rmse:0.490337 test-rmse:0.739190
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.334619 test-rmse:9.691025 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.573302 test-rmse:6.950515 [3] train-rmse:4.647752 test-rmse:4.937233 [4] train-rmse:3.307722 test-rmse:3.593834 [5] train-rmse:2.382201 test-rmse:2.618941 [6] train-rmse:1.743589 test-rmse:1.955262 [7] train-rmse:1.307575 test-rmse:1.519988 [8] train-rmse:1.019873 test-rmse:1.237751 [9] train-rmse:0.834304 test-rmse:1.045408 [10] train-rmse:0.702309 test-rmse:0.948090 [11] train-rmse:0.623582 test-rmse:0.883431 [12] train-rmse:0.573809 test-rmse:0.853043 [13] train-rmse:0.539535 test-rmse:0.834297 [14] train-rmse:0.522668 test-rmse:0.816989 [15] train-rmse:0.507225 test-rmse:0.811462 [16] train-rmse:0.501813 test-rmse:0.808743 [17] train-rmse:0.494188 test-rmse:0.816203 [18] train-rmse:0.485182 test-rmse:0.813913 [19] train-rmse:0.471413 test-rmse:0.816078 Stopping. Best iteration: [16] train-rmse:0.501813 test-rmse:0.808743 [1] train-rmse:9.328273 test-rmse:9.788075 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.565904 test-rmse:7.045877 [3] train-rmse:4.637515 test-rmse:5.157988 [4] train-rmse:3.295251 test-rmse:3.799758 [5] train-rmse:2.364557 test-rmse:2.886530 [6] train-rmse:1.724805 test-rmse:2.304634 [7] train-rmse:1.290972 test-rmse:1.931019 [8] train-rmse:0.996260 test-rmse:1.675413 [9] train-rmse:0.803432 test-rmse:1.525082 [10] train-rmse:0.688414 test-rmse:1.439141 [11] train-rmse:0.621746 test-rmse:1.390715 [12] train-rmse:0.572614 test-rmse:1.357026 [13] train-rmse:0.550018 test-rmse:1.333499 [14] train-rmse:0.535279 test-rmse:1.321728 [15] train-rmse:0.512550 test-rmse:1.309265 [16] train-rmse:0.507751 test-rmse:1.303161 [17] train-rmse:0.496160 test-rmse:1.300126 [18] train-rmse:0.491901 test-rmse:1.295379 [19] train-rmse:0.485247 test-rmse:1.296505 [20] train-rmse:0.480701 test-rmse:1.294944 [1] train-rmse:9.335798 test-rmse:9.884897 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.573254 test-rmse:7.165359 [3] train-rmse:4.641836 test-rmse:5.234345 [4] train-rmse:3.296963 test-rmse:3.903901 [5] train-rmse:2.365510 test-rmse:2.993282 [6] train-rmse:1.719965 test-rmse:2.366379 [7] train-rmse:1.278905 test-rmse:1.962798 [8] train-rmse:0.982999 test-rmse:1.681296 [9] train-rmse:0.787696 test-rmse:1.526469 [10] train-rmse:0.658949 test-rmse:1.419726 [11] train-rmse:0.586368 test-rmse:1.351173 [12] train-rmse:0.544466 test-rmse:1.303578 [13] train-rmse:0.512020 test-rmse:1.278783 [14] train-rmse:0.491655 test-rmse:1.259799 [15] train-rmse:0.479383 test-rmse:1.246438 [16] train-rmse:0.460156 test-rmse:1.240375 [17] train-rmse:0.448283 test-rmse:1.236727 [18] train-rmse:0.442336 test-rmse:1.231804 [19] train-rmse:0.439091 test-rmse:1.223743 [20] train-rmse:0.435941 test-rmse:1.220923 [1] train-rmse:9.416021 test-rmse:9.191758 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.629910 test-rmse:6.479389 [3] train-rmse:4.682681 test-rmse:4.540361 [4] train-rmse:3.327877 test-rmse:3.210889 [5] train-rmse:2.388909 test-rmse:2.343881 [6] train-rmse:1.744848 test-rmse:1.774207 [7] train-rmse:1.304251 test-rmse:1.405374 [8] train-rmse:1.016127 test-rmse:1.187742 [9] train-rmse:0.831484 test-rmse:1.053155 [10] train-rmse:0.711749 test-rmse:0.977052 [11] train-rmse:0.638704 test-rmse:0.935966 [12] train-rmse:0.594467 test-rmse:0.921136 [13] train-rmse:0.573137 test-rmse:0.913184 [14] train-rmse:0.554813 test-rmse:0.906297 [15] train-rmse:0.540741 test-rmse:0.907684 [16] train-rmse:0.524057 test-rmse:0.909077 [17] train-rmse:0.518661 test-rmse:0.912121 Stopping. Best iteration: [14] train-rmse:0.554813 test-rmse:0.906297 [1] train-rmse:9.435901 test-rmse:8.969103 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.642978 test-rmse:6.207485 [3] train-rmse:4.693714 test-rmse:4.291008 [4] train-rmse:3.336634 test-rmse:3.050338 [5] train-rmse:2.395549 test-rmse:2.191074 [6] train-rmse:1.746696 test-rmse:1.623674 [7] train-rmse:1.303630 test-rmse:1.277426 [8] train-rmse:1.013183 test-rmse:1.078744 [9] train-rmse:0.818375 test-rmse:0.985324 [10] train-rmse:0.691440 test-rmse:0.932491 [11] train-rmse:0.608103 test-rmse:0.917251 [12] train-rmse:0.558114 test-rmse:0.911213 [13] train-rmse:0.532576 test-rmse:0.911151 [14] train-rmse:0.515450 test-rmse:0.912112 [15] train-rmse:0.499575 test-rmse:0.909049 [16] train-rmse:0.490152 test-rmse:0.907359 [17] train-rmse:0.480527 test-rmse:0.906457 [18] train-rmse:0.473741 test-rmse:0.909715 [19] train-rmse:0.469105 test-rmse:0.911875 [20] train-rmse:0.463333 test-rmse:0.913101 Stopping. Best iteration: [17] train-rmse:0.480527 test-rmse:0.906457
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.869901 test-rmse:9.000906 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.218926 test-rmse:6.315073 [3] train-rmse:4.366184 test-rmse:4.430932 [4] train-rmse:3.073028 test-rmse:3.122710 [5] train-rmse:2.173357 test-rmse:2.216907 [6] train-rmse:1.550757 test-rmse:1.593873 [7] train-rmse:1.123668 test-rmse:1.169098 [8] train-rmse:0.836917 test-rmse:0.890516 [9] train-rmse:0.649977 test-rmse:0.709905 [10] train-rmse:0.532275 test-rmse:0.611986 [11] train-rmse:0.463194 test-rmse:0.553515 [12] train-rmse:0.423790 test-rmse:0.519316 [13] train-rmse:0.402267 test-rmse:0.500812 [14] train-rmse:0.390301 test-rmse:0.493903 [15] train-rmse:0.382573 test-rmse:0.487928 [16] train-rmse:0.375333 test-rmse:0.484937 [17] train-rmse:0.373488 test-rmse:0.484287 [18] train-rmse:0.371501 test-rmse:0.486377 [19] train-rmse:0.367828 test-rmse:0.485322 [20] train-rmse:0.366051 test-rmse:0.487129 Stopping. Best iteration: [17] train-rmse:0.373488 test-rmse:0.484287 [1] train-rmse:8.836334 test-rmse:9.144081 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.196070 test-rmse:6.427684 [3] train-rmse:4.350770 test-rmse:4.535501 [4] train-rmse:3.063578 test-rmse:3.212669 [5] train-rmse:2.168088 test-rmse:2.302561 [6] train-rmse:1.547913 test-rmse:1.661799 [7] train-rmse:1.124085 test-rmse:1.218860 [8] train-rmse:0.839657 test-rmse:0.928363 [9] train-rmse:0.655079 test-rmse:0.729354 [10] train-rmse:0.539447 test-rmse:0.608815 [11] train-rmse:0.472306 test-rmse:0.536995 [12] train-rmse:0.432704 test-rmse:0.492919 [13] train-rmse:0.411066 test-rmse:0.469385 [14] train-rmse:0.399129 test-rmse:0.455429 [15] train-rmse:0.392085 test-rmse:0.447352 [16] train-rmse:0.386430 test-rmse:0.441926 [17] train-rmse:0.384245 test-rmse:0.438825 [18] train-rmse:0.379749 test-rmse:0.437130 [19] train-rmse:0.378708 test-rmse:0.435603 [20] train-rmse:0.376214 test-rmse:0.435900 [1] train-rmse:8.882613 test-rmse:8.982517 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.227143 test-rmse:6.365198 [3] train-rmse:4.371425 test-rmse:4.522624 [4] train-rmse:3.076056 test-rmse:3.208666 [5] train-rmse:2.174410 test-rmse:2.324349 [6] train-rmse:1.550537 test-rmse:1.699673 [7] train-rmse:1.123217 test-rmse:1.279462 [8] train-rmse:0.835588 test-rmse:1.002266 [9] train-rmse:0.647545 test-rmse:0.824748 [10] train-rmse:0.529905 test-rmse:0.712511 [11] train-rmse:0.459544 test-rmse:0.641028 [12] train-rmse:0.419038 test-rmse:0.596479 [13] train-rmse:0.397821 test-rmse:0.574276 [14] train-rmse:0.384496 test-rmse:0.561687 [15] train-rmse:0.378200 test-rmse:0.554863 [16] train-rmse:0.374222 test-rmse:0.550403 [17] train-rmse:0.371649 test-rmse:0.546167 [18] train-rmse:0.370175 test-rmse:0.544587 [19] train-rmse:0.367798 test-rmse:0.542646 [20] train-rmse:0.367263 test-rmse:0.541806 [1] train-rmse:8.947743 test-rmse:8.628372 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.273923 test-rmse:6.015267 [3] train-rmse:4.404997 test-rmse:4.207025 [4] train-rmse:3.100845 test-rmse:2.955164 [5] train-rmse:2.193311 test-rmse:2.096736 [6] train-rmse:1.564075 test-rmse:1.524028 [7] train-rmse:1.133434 test-rmse:1.144298 [8] train-rmse:0.843373 test-rmse:0.914614 [9] train-rmse:0.654869 test-rmse:0.778125 [10] train-rmse:0.536541 test-rmse:0.709613 [11] train-rmse:0.464038 test-rmse:0.672838 [12] train-rmse:0.424238 test-rmse:0.657043 [13] train-rmse:0.399913 test-rmse:0.650649 [14] train-rmse:0.387666 test-rmse:0.648632 [15] train-rmse:0.381305 test-rmse:0.648685 [16] train-rmse:0.375991 test-rmse:0.648968 [17] train-rmse:0.369910 test-rmse:0.648673 Stopping. Best iteration: [14] train-rmse:0.387666 test-rmse:0.648632 [1] train-rmse:8.941187 test-rmse:8.644862 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.268834 test-rmse:6.019499 [3] train-rmse:4.400683 test-rmse:4.202441 [4] train-rmse:3.096700 test-rmse:2.942627 [5] train-rmse:2.188848 test-rmse:2.092401 [6] train-rmse:1.559168 test-rmse:1.524528 [7] train-rmse:1.127604 test-rmse:1.154732 [8] train-rmse:0.837320 test-rmse:0.927472 [9] train-rmse:0.645967 test-rmse:0.809087 [10] train-rmse:0.525770 test-rmse:0.748451 [11] train-rmse:0.454046 test-rmse:0.720254 [12] train-rmse:0.412604 test-rmse:0.709260 [13] train-rmse:0.391050 test-rmse:0.705231 [14] train-rmse:0.376015 test-rmse:0.704511 [15] train-rmse:0.366602 test-rmse:0.705449 [16] train-rmse:0.362549 test-rmse:0.706195 [17] train-rmse:0.358386 test-rmse:0.706928 Stopping. Best iteration: [14] train-rmse:0.376015 test-rmse:0.704511
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.976913 test-rmse:9.030484 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.295620 test-rmse:6.307334 [3] train-rmse:4.422196 test-rmse:4.401740 [4] train-rmse:3.115770 test-rmse:3.114716 [5] train-rmse:2.207857 test-rmse:2.213892 [6] train-rmse:1.581273 test-rmse:1.622722 [7] train-rmse:1.154051 test-rmse:1.242911 [8] train-rmse:0.870202 test-rmse:1.023529 [9] train-rmse:0.685638 test-rmse:0.912935 [10] train-rmse:0.571154 test-rmse:0.861869 [11] train-rmse:0.504926 test-rmse:0.841863 [12] train-rmse:0.465569 test-rmse:0.837719 [13] train-rmse:0.442581 test-rmse:0.839865 [14] train-rmse:0.429221 test-rmse:0.841031 [15] train-rmse:0.421441 test-rmse:0.844362 Stopping. Best iteration: [12] train-rmse:0.465569 test-rmse:0.837719 [1] train-rmse:8.970403 test-rmse:9.162219 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.293292 test-rmse:6.439381 [3] train-rmse:4.423842 test-rmse:4.537115 [4] train-rmse:3.121475 test-rmse:3.219602 [5] train-rmse:2.218427 test-rmse:2.300798 [6] train-rmse:1.596485 test-rmse:1.673592 [7] train-rmse:1.170843 test-rmse:1.260436 [8] train-rmse:0.889869 test-rmse:0.977185 [9] train-rmse:0.705827 test-rmse:0.801012 [10] train-rmse:0.594531 test-rmse:0.688307 [11] train-rmse:0.529237 test-rmse:0.623787 [12] train-rmse:0.492896 test-rmse:0.581378 [13] train-rmse:0.467187 test-rmse:0.566386 [14] train-rmse:0.454639 test-rmse:0.553021 [15] train-rmse:0.448978 test-rmse:0.544919 [16] train-rmse:0.443868 test-rmse:0.538488 [17] train-rmse:0.440510 test-rmse:0.536000 [18] train-rmse:0.436442 test-rmse:0.536321 [19] train-rmse:0.429680 test-rmse:0.535389 [20] train-rmse:0.424324 test-rmse:0.533093 [1] train-rmse:8.990635 test-rmse:9.022073 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.306390 test-rmse:6.338675 [3] train-rmse:4.431442 test-rmse:4.472449 [4] train-rmse:3.124660 test-rmse:3.181340 [5] train-rmse:2.217692 test-rmse:2.278817 [6] train-rmse:1.592266 test-rmse:1.661717 [7] train-rmse:1.168138 test-rmse:1.248248 [8] train-rmse:0.881872 test-rmse:0.983725 [9] train-rmse:0.696670 test-rmse:0.826522 [10] train-rmse:0.583707 test-rmse:0.737861 [11] train-rmse:0.517799 test-rmse:0.689497 [12] train-rmse:0.477217 test-rmse:0.663310 [13] train-rmse:0.455285 test-rmse:0.651570 [14] train-rmse:0.443185 test-rmse:0.647249 [15] train-rmse:0.434961 test-rmse:0.645104 [16] train-rmse:0.426304 test-rmse:0.642071 [17] train-rmse:0.423860 test-rmse:0.642013 [18] train-rmse:0.421583 test-rmse:0.642830 [19] train-rmse:0.414724 test-rmse:0.642932 [20] train-rmse:0.413911 test-rmse:0.642691 Stopping. Best iteration: [17] train-rmse:0.423860 test-rmse:0.642013 [1] train-rmse:9.028595 test-rmse:8.841879 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.333463 test-rmse:6.205606 [3] train-rmse:4.451148 test-rmse:4.365885 [4] train-rmse:3.139269 test-rmse:3.065589 [5] train-rmse:2.228909 test-rmse:2.192611 [6] train-rmse:1.601324 test-rmse:1.607840 [7] train-rmse:1.171834 test-rmse:1.228214 [8] train-rmse:0.888232 test-rmse:1.000595 [9] train-rmse:0.702948 test-rmse:0.877738 [10] train-rmse:0.589601 test-rmse:0.815954 [11] train-rmse:0.522295 test-rmse:0.785575 [12] train-rmse:0.484848 test-rmse:0.772651 [13] train-rmse:0.461565 test-rmse:0.770968 [14] train-rmse:0.448455 test-rmse:0.770617 [15] train-rmse:0.441247 test-rmse:0.771568 [16] train-rmse:0.437382 test-rmse:0.772569 [17] train-rmse:0.433076 test-rmse:0.773384 Stopping. Best iteration: [14] train-rmse:0.448455 test-rmse:0.770617 [1] train-rmse:9.021105 test-rmse:8.894038 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.328256 test-rmse:6.244295 [3] train-rmse:4.447507 test-rmse:4.393521 [4] train-rmse:3.136790 test-rmse:3.103016 [5] train-rmse:2.227136 test-rmse:2.213429 [6] train-rmse:1.601409 test-rmse:1.609975 [7] train-rmse:1.172866 test-rmse:1.211850 [8] train-rmse:0.888676 test-rmse:0.959814 [9] train-rmse:0.704709 test-rmse:0.819586 [10] train-rmse:0.592051 test-rmse:0.737389 [11] train-rmse:0.523511 test-rmse:0.695382 [12] train-rmse:0.485655 test-rmse:0.672999 [13] train-rmse:0.465211 test-rmse:0.663904 [14] train-rmse:0.452336 test-rmse:0.659869 [15] train-rmse:0.441655 test-rmse:0.658988 [16] train-rmse:0.434694 test-rmse:0.657817 [17] train-rmse:0.429626 test-rmse:0.658316 [18] train-rmse:0.427318 test-rmse:0.658879 [19] train-rmse:0.424181 test-rmse:0.659433 Stopping. Best iteration: [16] train-rmse:0.434694 test-rmse:0.657817
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.573519 test-rmse:8.303055 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.051748 test-rmse:5.794902 [3] train-rmse:4.288547 test-rmse:4.052012 [4] train-rmse:3.046811 test-rmse:2.856314 [5] train-rmse:2.178181 test-rmse:2.025220 [6] train-rmse:1.571218 test-rmse:1.463972 [7] train-rmse:1.153838 test-rmse:1.099815 [8] train-rmse:0.866909 test-rmse:0.903848 [9] train-rmse:0.671823 test-rmse:0.763484 [10] train-rmse:0.525922 test-rmse:0.711254 [11] train-rmse:0.418177 test-rmse:0.680587 [12] train-rmse:0.342502 test-rmse:0.680535 [13] train-rmse:0.291023 test-rmse:0.675411 [14] train-rmse:0.253821 test-rmse:0.677589 [15] train-rmse:0.227195 test-rmse:0.677881 [16] train-rmse:0.208831 test-rmse:0.685454 Stopping. Best iteration: [13] train-rmse:0.291023 test-rmse:0.675411 [1] train-rmse:8.662566 test-rmse:7.811053 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.115185 test-rmse:5.253593 [3] train-rmse:4.330695 test-rmse:3.693125 [4] train-rmse:3.079269 test-rmse:2.427833 [5] train-rmse:2.202468 test-rmse:1.694694 [6] train-rmse:1.592544 test-rmse:1.200259 [7] train-rmse:1.173470 test-rmse:0.815787 [8] train-rmse:0.884157 test-rmse:0.610699 [9] train-rmse:0.689619 test-rmse:0.473126 [10] train-rmse:0.543091 test-rmse:0.422594 [11] train-rmse:0.443806 test-rmse:0.389099 [12] train-rmse:0.374108 test-rmse:0.369197 [13] train-rmse:0.327095 test-rmse:0.341752 [14] train-rmse:0.284257 test-rmse:0.352857 [15] train-rmse:0.257466 test-rmse:0.343086 [16] train-rmse:0.239404 test-rmse:0.363679 Stopping. Best iteration: [13] train-rmse:0.327095 test-rmse:0.341752 [1] train-rmse:8.493035 test-rmse:8.884759 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.000110 test-rmse:6.379416 [3] train-rmse:4.249930 test-rmse:4.415771 [4] train-rmse:3.018740 test-rmse:3.032956 [5] train-rmse:2.156034 test-rmse:2.063747 [6] train-rmse:1.555893 test-rmse:1.418786 [7] train-rmse:1.143873 test-rmse:0.958402 [8] train-rmse:0.864256 test-rmse:0.658107 [9] train-rmse:0.675724 test-rmse:0.478100 [10] train-rmse:0.535252 test-rmse:0.433538 [11] train-rmse:0.442505 test-rmse:0.394217 [12] train-rmse:0.374676 test-rmse:0.393299 [13] train-rmse:0.326922 test-rmse:0.414816 [14] train-rmse:0.296769 test-rmse:0.435410 [15] train-rmse:0.275616 test-rmse:0.460438 Stopping. Best iteration: [12] train-rmse:0.374676 test-rmse:0.393299 [1] train-rmse:8.529848 test-rmse:8.698165 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.021866 test-rmse:6.196264 [3] train-rmse:4.261803 test-rmse:4.536107 [4] train-rmse:3.023820 test-rmse:3.373971 [5] train-rmse:2.155186 test-rmse:2.542917 [6] train-rmse:1.549782 test-rmse:1.968575 [7] train-rmse:1.132527 test-rmse:1.578082 [8] train-rmse:0.847138 test-rmse:1.297174 [9] train-rmse:0.653082 test-rmse:1.107333 [10] train-rmse:0.511056 test-rmse:0.999271 [11] train-rmse:0.412786 test-rmse:0.944009 [12] train-rmse:0.348468 test-rmse:0.911495 [13] train-rmse:0.303400 test-rmse:0.866213 [14] train-rmse:0.264204 test-rmse:0.835813 [15] train-rmse:0.233693 test-rmse:0.822349 [16] train-rmse:0.218767 test-rmse:0.815579 [17] train-rmse:0.203540 test-rmse:0.812973 [18] train-rmse:0.193887 test-rmse:0.826070 [19] train-rmse:0.188601 test-rmse:0.830276 [20] train-rmse:0.184569 test-rmse:0.838194 Stopping. Best iteration: [17] train-rmse:0.203540 test-rmse:0.812973 [1] train-rmse:8.478056 test-rmse:8.939548 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.985711 test-rmse:6.446171 [3] train-rmse:4.238306 test-rmse:4.563408 [4] train-rmse:3.008878 test-rmse:3.243598 [5] train-rmse:2.145991 test-rmse:2.314073 [6] train-rmse:1.543656 test-rmse:1.672448 [7] train-rmse:1.127772 test-rmse:1.253839 [8] train-rmse:0.839783 test-rmse:0.946948 [9] train-rmse:0.642772 test-rmse:0.787022 [10] train-rmse:0.497453 test-rmse:0.676278 [11] train-rmse:0.398315 test-rmse:0.642994 [12] train-rmse:0.333775 test-rmse:0.651792 [13] train-rmse:0.290242 test-rmse:0.666500 [14] train-rmse:0.249158 test-rmse:0.673553 Stopping. Best iteration: [11] train-rmse:0.398315 test-rmse:0.642994
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.341174 test-rmse:8.021919 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.908005 test-rmse:5.590281 [3] train-rmse:4.190332 test-rmse:3.875126 [4] train-rmse:2.980085 test-rmse:2.669165 [5] train-rmse:2.130573 test-rmse:1.827211 [6] train-rmse:1.538592 test-rmse:1.248906 [7] train-rmse:1.130200 test-rmse:0.850389 [8] train-rmse:0.852099 test-rmse:0.621709 [9] train-rmse:0.660633 test-rmse:0.453550 [10] train-rmse:0.532564 test-rmse:0.388788 [11] train-rmse:0.450300 test-rmse:0.390595 [12] train-rmse:0.399834 test-rmse:0.419031 [13] train-rmse:0.370182 test-rmse:0.451829 Stopping. Best iteration: [10] train-rmse:0.532564 test-rmse:0.388788 [1] train-rmse:8.326715 test-rmse:8.156098 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.899113 test-rmse:5.725345 [3] train-rmse:4.185922 test-rmse:4.007790 [4] train-rmse:2.979598 test-rmse:2.795500 [5] train-rmse:2.133885 test-rmse:1.941815 [6] train-rmse:1.545933 test-rmse:1.343651 [7] train-rmse:1.143454 test-rmse:0.929108 [8] train-rmse:0.864110 test-rmse:0.667253 [9] train-rmse:0.673583 test-rmse:0.498227 [10] train-rmse:0.547888 test-rmse:0.393507 [11] train-rmse:0.468287 test-rmse:0.337422 [12] train-rmse:0.419518 test-rmse:0.312606 [13] train-rmse:0.390357 test-rmse:0.304205 [14] train-rmse:0.373683 test-rmse:0.303635 [15] train-rmse:0.364330 test-rmse:0.305960 [16] train-rmse:0.359115 test-rmse:0.308635 [17] train-rmse:0.356170 test-rmse:0.311053 Stopping. Best iteration: [14] train-rmse:0.373683 test-rmse:0.303635 [1] train-rmse:8.305666 test-rmse:8.268465 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.884121 test-rmse:5.845323 [3] train-rmse:4.175172 test-rmse:4.134151 [4] train-rmse:2.971790 test-rmse:2.927708 [5] train-rmse:2.128078 test-rmse:2.079857 [6] train-rmse:1.541436 test-rmse:1.487797 [7] train-rmse:1.139754 test-rmse:1.079418 [8] train-rmse:0.860196 test-rmse:0.826783 [9] train-rmse:0.668646 test-rmse:0.661587 [10] train-rmse:0.541959 test-rmse:0.554288 [11] train-rmse:0.461376 test-rmse:0.489530 [12] train-rmse:0.411545 test-rmse:0.450415 [13] train-rmse:0.381698 test-rmse:0.427826 [14] train-rmse:0.364540 test-rmse:0.415040 [15] train-rmse:0.354875 test-rmse:0.407496 [16] train-rmse:0.349510 test-rmse:0.403074 [17] train-rmse:0.346548 test-rmse:0.400455 [18] train-rmse:0.344917 test-rmse:0.398812 [19] train-rmse:0.344019 test-rmse:0.397776 [20] train-rmse:0.343525 test-rmse:0.397114 [1] train-rmse:8.219663 test-rmse:8.786036 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.821537 test-rmse:6.384151 [3] train-rmse:4.128438 test-rmse:4.685694 [4] train-rmse:2.935275 test-rmse:3.484896 [5] train-rmse:2.097440 test-rmse:2.636220 [6] train-rmse:1.513177 test-rmse:2.036705 [7] train-rmse:1.110993 test-rmse:1.613487 [8] train-rmse:0.840410 test-rmse:1.314971 [9] train-rmse:0.652735 test-rmse:1.071530 [10] train-rmse:0.526973 test-rmse:0.897086 [11] train-rmse:0.445792 test-rmse:0.773875 [12] train-rmse:0.395875 test-rmse:0.686410 [13] train-rmse:0.364551 test-rmse:0.621934 [14] train-rmse:0.344848 test-rmse:0.578377 [15] train-rmse:0.332583 test-rmse:0.549480 [16] train-rmse:0.325006 test-rmse:0.531089 [17] train-rmse:0.320332 test-rmse:0.519883 [18] train-rmse:0.317446 test-rmse:0.513144 [19] train-rmse:0.315661 test-rmse:0.509434 [20] train-rmse:0.314551 test-rmse:0.507472 [1] train-rmse:8.317634 test-rmse:8.179597 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.890654 test-rmse:5.757074 [3] train-rmse:4.177073 test-rmse:4.049969 [4] train-rmse:2.969322 test-rmse:2.851678 [5] train-rmse:2.121032 test-rmse:2.017232 [6] train-rmse:1.529196 test-rmse:1.445489 [7] train-rmse:1.121441 test-rmse:1.065989 [8] train-rmse:0.845375 test-rmse:0.825430 [9] train-rmse:0.660321 test-rmse:0.653876 [10] train-rmse:0.539415 test-rmse:0.588571 [11] train-rmse:0.464122 test-rmse:0.587392 [12] train-rmse:0.419333 test-rmse:0.613285 [13] train-rmse:0.393497 test-rmse:0.654015 [14] train-rmse:0.378651 test-rmse:0.694778 Stopping. Best iteration: [11] train-rmse:0.464122 test-rmse:0.587392
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.466039 test-rmse:8.465558 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.938439 test-rmse:5.943923 [3] train-rmse:4.170215 test-rmse:4.184163 [4] train-rmse:2.935223 test-rmse:2.961088 [5] train-rmse:2.075449 test-rmse:2.117832 [6] train-rmse:1.478868 test-rmse:1.558016 [7] train-rmse:1.067559 test-rmse:1.173740 [8] train-rmse:0.786238 test-rmse:0.948479 [9] train-rmse:0.597697 test-rmse:0.803723 [10] train-rmse:0.471172 test-rmse:0.734389 [11] train-rmse:0.394302 test-rmse:0.692831 [12] train-rmse:0.347057 test-rmse:0.685891 [13] train-rmse:0.319048 test-rmse:0.686477 [14] train-rmse:0.304261 test-rmse:0.689588 [15] train-rmse:0.295961 test-rmse:0.692234 Stopping. Best iteration: [12] train-rmse:0.347057 test-rmse:0.685891 [1] train-rmse:8.483954 test-rmse:8.351552 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.950312 test-rmse:5.823750 [3] train-rmse:4.177577 test-rmse:4.059524 [4] train-rmse:2.939030 test-rmse:2.833413 [5] train-rmse:2.076218 test-rmse:1.988765 [6] train-rmse:1.478617 test-rmse:1.417357 [7] train-rmse:1.065589 test-rmse:1.144236 [8] train-rmse:0.783773 test-rmse:0.973724 [9] train-rmse:0.595266 test-rmse:0.857431 [10] train-rmse:0.473759 test-rmse:0.790783 [11] train-rmse:0.398153 test-rmse:0.735680 [12] train-rmse:0.351024 test-rmse:0.712706 [13] train-rmse:0.324059 test-rmse:0.692767 [14] train-rmse:0.309268 test-rmse:0.683985 [15] train-rmse:0.300824 test-rmse:0.675779 [16] train-rmse:0.296747 test-rmse:0.676360 [17] train-rmse:0.294566 test-rmse:0.678104 [18] train-rmse:0.292877 test-rmse:0.677598 Stopping. Best iteration: [15] train-rmse:0.300824 test-rmse:0.675779 [1] train-rmse:8.455255 test-rmse:8.537645 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.930309 test-rmse:6.019253 [3] train-rmse:4.163712 test-rmse:4.261814 [4] train-rmse:2.929527 test-rmse:3.040253 [5] train-rmse:2.068790 test-rmse:2.176258 [6] train-rmse:1.471069 test-rmse:1.591244 [7] train-rmse:1.059937 test-rmse:1.206887 [8] train-rmse:0.781008 test-rmse:0.970693 [9] train-rmse:0.594671 test-rmse:0.834298 [10] train-rmse:0.472446 test-rmse:0.754863 [11] train-rmse:0.397979 test-rmse:0.717142 [12] train-rmse:0.351738 test-rmse:0.697008 [13] train-rmse:0.324564 test-rmse:0.680770 [14] train-rmse:0.310203 test-rmse:0.672618 [15] train-rmse:0.302571 test-rmse:0.671186 [16] train-rmse:0.298370 test-rmse:0.670936 [17] train-rmse:0.295489 test-rmse:0.670603 [18] train-rmse:0.293694 test-rmse:0.670513 [19] train-rmse:0.292629 test-rmse:0.670556 [20] train-rmse:0.291902 test-rmse:0.670769 [1] train-rmse:8.485000 test-rmse:8.354339 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.950431 test-rmse:5.827547 [3] train-rmse:4.176788 test-rmse:4.065189 [4] train-rmse:2.937238 test-rmse:2.842042 [5] train-rmse:2.073215 test-rmse:2.001759 [6] train-rmse:1.473186 test-rmse:1.593462 [7] train-rmse:1.058797 test-rmse:1.234758 [8] train-rmse:0.776914 test-rmse:0.979003 [9] train-rmse:0.585002 test-rmse:0.889179 [10] train-rmse:0.458301 test-rmse:0.839625 [11] train-rmse:0.376877 test-rmse:0.813490 [12] train-rmse:0.328710 test-rmse:0.797580 [13] train-rmse:0.300585 test-rmse:0.781275 [14] train-rmse:0.280837 test-rmse:0.771431 [15] train-rmse:0.272226 test-rmse:0.764918 [16] train-rmse:0.265233 test-rmse:0.758426 [17] train-rmse:0.261671 test-rmse:0.754505 [18] train-rmse:0.259578 test-rmse:0.753594 [19] train-rmse:0.258206 test-rmse:0.753117 [20] train-rmse:0.257615 test-rmse:0.752500 [1] train-rmse:8.460879 test-rmse:8.501479 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.934252 test-rmse:5.978778 [3] train-rmse:4.166475 test-rmse:4.216527 [4] train-rmse:2.931465 test-rmse:2.989214 [5] train-rmse:2.071220 test-rmse:2.139495 [6] train-rmse:1.474302 test-rmse:1.529195 [7] train-rmse:1.062712 test-rmse:1.120237 [8] train-rmse:0.782496 test-rmse:0.862527 [9] train-rmse:0.595145 test-rmse:0.707842 [10] train-rmse:0.474198 test-rmse:0.626853 [11] train-rmse:0.398964 test-rmse:0.584006 [12] train-rmse:0.354853 test-rmse:0.569275 [13] train-rmse:0.328826 test-rmse:0.565112 [14] train-rmse:0.314904 test-rmse:0.568284 [15] train-rmse:0.306544 test-rmse:0.569742 [16] train-rmse:0.302428 test-rmse:0.577657 Stopping. Best iteration: [13] train-rmse:0.328826 test-rmse:0.565112
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.860887 test-rmse:9.419149 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.250703 test-rmse:6.827483 [3] train-rmse:4.433660 test-rmse:5.033203 [4] train-rmse:3.169367 test-rmse:3.799752 [5] train-rmse:2.294868 test-rmse:2.941979 [6] train-rmse:1.698965 test-rmse:2.357342 [7] train-rmse:1.299773 test-rmse:1.984113 [8] train-rmse:1.029146 test-rmse:1.767883 [9] train-rmse:0.847393 test-rmse:1.681458 [10] train-rmse:0.725708 test-rmse:1.634107 [11] train-rmse:0.657118 test-rmse:1.562943 [12] train-rmse:0.600751 test-rmse:1.551063 [13] train-rmse:0.571290 test-rmse:1.521079 [14] train-rmse:0.543471 test-rmse:1.515043 [15] train-rmse:0.529980 test-rmse:1.500631 [16] train-rmse:0.493580 test-rmse:1.533181 [17] train-rmse:0.483099 test-rmse:1.530591 [18] train-rmse:0.461887 test-rmse:1.556146 Stopping. Best iteration: [15] train-rmse:0.529980 test-rmse:1.500631 [1] train-rmse:8.937187 test-rmse:8.678488 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.305333 test-rmse:6.048221 [3] train-rmse:4.473530 test-rmse:4.219601 [4] train-rmse:3.200148 test-rmse:2.983839 [5] train-rmse:2.321718 test-rmse:2.132841 [6] train-rmse:1.721869 test-rmse:1.578593 [7] train-rmse:1.325198 test-rmse:1.212714 [8] train-rmse:1.058388 test-rmse:0.996531 [9] train-rmse:0.883610 test-rmse:0.880659 [10] train-rmse:0.759240 test-rmse:0.839522 [11] train-rmse:0.683744 test-rmse:0.816181 [12] train-rmse:0.621439 test-rmse:0.813751 [13] train-rmse:0.589983 test-rmse:0.796727 [14] train-rmse:0.561286 test-rmse:0.798184 [15] train-rmse:0.536319 test-rmse:0.814588 [16] train-rmse:0.522133 test-rmse:0.825930 Stopping. Best iteration: [13] train-rmse:0.589983 test-rmse:0.796727 [1] train-rmse:8.933369 test-rmse:8.750484 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.299910 test-rmse:6.129614 [3] train-rmse:4.465929 test-rmse:4.314132 [4] train-rmse:3.190187 test-rmse:3.054228 [5] train-rmse:2.305255 test-rmse:2.217621 [6] train-rmse:1.700880 test-rmse:1.662229 [7] train-rmse:1.293358 test-rmse:1.311413 [8] train-rmse:1.020338 test-rmse:1.110644 [9] train-rmse:0.847419 test-rmse:1.012972 [10] train-rmse:0.719520 test-rmse:0.965043 [11] train-rmse:0.636808 test-rmse:0.949792 [12] train-rmse:0.579339 test-rmse:0.979766 [13] train-rmse:0.537455 test-rmse:0.993997 [14] train-rmse:0.512845 test-rmse:1.003796 Stopping. Best iteration: [11] train-rmse:0.636808 test-rmse:0.949792 [1] train-rmse:8.880458 test-rmse:8.999301 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.263663 test-rmse:6.399773 [3] train-rmse:4.437692 test-rmse:4.586054 [4] train-rmse:3.165168 test-rmse:3.348353 [5] train-rmse:2.284571 test-rmse:2.519633 [6] train-rmse:1.681803 test-rmse:1.969879 [7] train-rmse:1.274258 test-rmse:1.654662 [8] train-rmse:1.002952 test-rmse:1.470733 [9] train-rmse:0.818553 test-rmse:1.378830 [10] train-rmse:0.691455 test-rmse:1.306326 [11] train-rmse:0.602336 test-rmse:1.262812 [12] train-rmse:0.549626 test-rmse:1.231712 [13] train-rmse:0.516430 test-rmse:1.225637 [14] train-rmse:0.477965 test-rmse:1.222800 [15] train-rmse:0.459986 test-rmse:1.216617 [16] train-rmse:0.432568 test-rmse:1.219221 [17] train-rmse:0.419405 test-rmse:1.221503 [18] train-rmse:0.412373 test-rmse:1.220816 Stopping. Best iteration: [15] train-rmse:0.459986 test-rmse:1.216617 [1] train-rmse:8.882510 test-rmse:9.108497 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.265134 test-rmse:6.516903 [3] train-rmse:4.442749 test-rmse:4.728793 [4] train-rmse:3.175544 test-rmse:3.478996 [5] train-rmse:2.299533 test-rmse:2.610101 [6] train-rmse:1.702312 test-rmse:2.054795 [7] train-rmse:1.299836 test-rmse:1.738892 [8] train-rmse:1.035057 test-rmse:1.536209 [9] train-rmse:0.838454 test-rmse:1.429827 [10] train-rmse:0.711180 test-rmse:1.358530 [11] train-rmse:0.636733 test-rmse:1.336818 [12] train-rmse:0.583345 test-rmse:1.310348 [13] train-rmse:0.524581 test-rmse:1.299689 [14] train-rmse:0.493294 test-rmse:1.305720 [15] train-rmse:0.473576 test-rmse:1.313716 [16] train-rmse:0.462002 test-rmse:1.308683 Stopping. Best iteration: [13] train-rmse:0.524581 test-rmse:1.299689
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.780199 test-rmse:8.720125 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.154999 test-rmse:6.105195 [3] train-rmse:4.319520 test-rmse:4.271056 [4] train-rmse:3.037515 test-rmse:2.993212 [5] train-rmse:2.144132 test-rmse:2.100336 [6] train-rmse:1.524904 test-rmse:1.490322 [7] train-rmse:1.099304 test-rmse:1.071757 [8] train-rmse:0.811543 test-rmse:0.798763 [9] train-rmse:0.621784 test-rmse:0.624723 [10] train-rmse:0.501763 test-rmse:0.522374 [11] train-rmse:0.428616 test-rmse:0.467418 [12] train-rmse:0.387009 test-rmse:0.441223 [13] train-rmse:0.364074 test-rmse:0.429346 [14] train-rmse:0.350478 test-rmse:0.422793 [15] train-rmse:0.342973 test-rmse:0.420906 [16] train-rmse:0.338482 test-rmse:0.420654 [17] train-rmse:0.335025 test-rmse:0.420112 [18] train-rmse:0.332381 test-rmse:0.421538 [19] train-rmse:0.328749 test-rmse:0.423488 [20] train-rmse:0.327216 test-rmse:0.423518 Stopping. Best iteration: [17] train-rmse:0.335025 test-rmse:0.420112 [1] train-rmse:8.762284 test-rmse:8.832029 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.142087 test-rmse:6.216422 [3] train-rmse:4.309922 test-rmse:4.385150 [4] train-rmse:3.029831 test-rmse:3.103266 [5] train-rmse:2.137753 test-rmse:2.217029 [6] train-rmse:1.518692 test-rmse:1.599094 [7] train-rmse:1.092976 test-rmse:1.177739 [8] train-rmse:0.803684 test-rmse:0.895047 [9] train-rmse:0.611720 test-rmse:0.709018 [10] train-rmse:0.489287 test-rmse:0.590252 [11] train-rmse:0.414124 test-rmse:0.518153 [12] train-rmse:0.369690 test-rmse:0.475478 [13] train-rmse:0.344940 test-rmse:0.451848 [14] train-rmse:0.331487 test-rmse:0.438299 [15] train-rmse:0.323986 test-rmse:0.429717 [16] train-rmse:0.319793 test-rmse:0.424540 [17] train-rmse:0.317341 test-rmse:0.422164 [18] train-rmse:0.312349 test-rmse:0.418961 [19] train-rmse:0.309726 test-rmse:0.417856 [20] train-rmse:0.309298 test-rmse:0.416995 [1] train-rmse:8.785598 test-rmse:8.670729 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.157600 test-rmse:6.048097 [3] train-rmse:4.319709 test-rmse:4.220972 [4] train-rmse:3.035725 test-rmse:2.954457 [5] train-rmse:2.140634 test-rmse:2.086536 [6] train-rmse:1.519613 test-rmse:1.490743 [7] train-rmse:1.091872 test-rmse:1.105253 [8] train-rmse:0.801396 test-rmse:0.868046 [9] train-rmse:0.609906 test-rmse:0.729086 [10] train-rmse:0.486649 test-rmse:0.655173 [11] train-rmse:0.411603 test-rmse:0.621375 [12] train-rmse:0.367763 test-rmse:0.608024 [13] train-rmse:0.341490 test-rmse:0.604434 [14] train-rmse:0.328015 test-rmse:0.602191 [15] train-rmse:0.320106 test-rmse:0.603768 [16] train-rmse:0.315460 test-rmse:0.606180 [17] train-rmse:0.312962 test-rmse:0.605057 Stopping. Best iteration: [14] train-rmse:0.328015 test-rmse:0.602191 [1] train-rmse:8.764179 test-rmse:8.814554 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.143318 test-rmse:6.185514 [3] train-rmse:4.310631 test-rmse:4.351260 [4] train-rmse:3.030592 test-rmse:3.067822 [5] train-rmse:2.138688 test-rmse:2.174898 [6] train-rmse:1.520168 test-rmse:1.560197 [7] train-rmse:1.094973 test-rmse:1.147041 [8] train-rmse:0.805826 test-rmse:0.874141 [9] train-rmse:0.615013 test-rmse:0.703153 [10] train-rmse:0.493247 test-rmse:0.604714 [11] train-rmse:0.419208 test-rmse:0.550219 [12] train-rmse:0.375015 test-rmse:0.522597 [13] train-rmse:0.350544 test-rmse:0.510361 [14] train-rmse:0.336846 test-rmse:0.504536 [15] train-rmse:0.326851 test-rmse:0.502367 [16] train-rmse:0.321048 test-rmse:0.504320 [17] train-rmse:0.315744 test-rmse:0.505196 [18] train-rmse:0.310348 test-rmse:0.505844 Stopping. Best iteration: [15] train-rmse:0.326851 test-rmse:0.502367 [1] train-rmse:8.770873 test-rmse:8.758064 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.147899 test-rmse:6.141793 [3] train-rmse:4.313589 test-rmse:4.319681 [4] train-rmse:3.032176 test-rmse:3.049767 [5] train-rmse:2.139155 test-rmse:2.178904 [6] train-rmse:1.518896 test-rmse:1.590082 [7] train-rmse:1.092025 test-rmse:1.209505 [8] train-rmse:0.802206 test-rmse:0.974693 [9] train-rmse:0.609979 test-rmse:0.837378 [10] train-rmse:0.487063 test-rmse:0.767425 [11] train-rmse:0.409832 test-rmse:0.736172 [12] train-rmse:0.365443 test-rmse:0.721062 [13] train-rmse:0.339787 test-rmse:0.715691 [14] train-rmse:0.326528 test-rmse:0.713816 [15] train-rmse:0.316942 test-rmse:0.713924 [16] train-rmse:0.313173 test-rmse:0.715123 [17] train-rmse:0.311175 test-rmse:0.716221 Stopping. Best iteration: [14] train-rmse:0.326528 test-rmse:0.713816
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.875638 test-rmse:8.946206 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.226021 test-rmse:6.299860 [3] train-rmse:4.374260 test-rmse:4.443817 [4] train-rmse:3.081797 test-rmse:3.160653 [5] train-rmse:2.183026 test-rmse:2.268455 [6] train-rmse:1.560362 test-rmse:1.672028 [7] train-rmse:1.135527 test-rmse:1.270531 [8] train-rmse:0.850528 test-rmse:1.010651 [9] train-rmse:0.660093 test-rmse:0.854734 [10] train-rmse:0.538961 test-rmse:0.764481 [11] train-rmse:0.464695 test-rmse:0.714160 [12] train-rmse:0.418658 test-rmse:0.687306 [13] train-rmse:0.395336 test-rmse:0.673840 [14] train-rmse:0.377020 test-rmse:0.668008 [15] train-rmse:0.368210 test-rmse:0.664556 [16] train-rmse:0.362586 test-rmse:0.662326 [17] train-rmse:0.355396 test-rmse:0.661942 [18] train-rmse:0.352840 test-rmse:0.661102 [19] train-rmse:0.344400 test-rmse:0.662135 [20] train-rmse:0.339132 test-rmse:0.662596 [1] train-rmse:8.893708 test-rmse:8.755706 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.237265 test-rmse:6.131278 [3] train-rmse:4.380857 test-rmse:4.320721 [4] train-rmse:3.084982 test-rmse:3.090445 [5] train-rmse:2.182705 test-rmse:2.269740 [6] train-rmse:1.558199 test-rmse:1.760291 [7] train-rmse:1.130832 test-rmse:1.465161 [8] train-rmse:0.842214 test-rmse:1.315411 [9] train-rmse:0.649356 test-rmse:1.248959 [10] train-rmse:0.526172 test-rmse:1.221828 [11] train-rmse:0.451176 test-rmse:1.219179 [12] train-rmse:0.405273 test-rmse:1.207018 [13] train-rmse:0.378194 test-rmse:1.211618 [14] train-rmse:0.360985 test-rmse:1.216795 [15] train-rmse:0.350776 test-rmse:1.220542 Stopping. Best iteration: [12] train-rmse:0.405273 test-rmse:1.207018 [1] train-rmse:8.898838 test-rmse:8.798266 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.241273 test-rmse:6.146724 [3] train-rmse:4.384254 test-rmse:4.298433 [4] train-rmse:3.088838 test-rmse:3.028685 [5] train-rmse:2.187579 test-rmse:2.146969 [6] train-rmse:1.562761 test-rmse:1.550418 [7] train-rmse:1.136479 test-rmse:1.160940 [8] train-rmse:0.848967 test-rmse:0.916494 [9] train-rmse:0.662558 test-rmse:0.776303 [10] train-rmse:0.541586 test-rmse:0.701614 [11] train-rmse:0.468921 test-rmse:0.667522 [12] train-rmse:0.418791 test-rmse:0.648773 [13] train-rmse:0.390605 test-rmse:0.644482 [14] train-rmse:0.376882 test-rmse:0.642977 [15] train-rmse:0.364486 test-rmse:0.645312 [16] train-rmse:0.357135 test-rmse:0.646834 [17] train-rmse:0.353808 test-rmse:0.649098 Stopping. Best iteration: [14] train-rmse:0.376882 test-rmse:0.642977 [1] train-rmse:8.894723 test-rmse:8.806874 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.238277 test-rmse:6.164674 [3] train-rmse:4.381995 test-rmse:4.328818 [4] train-rmse:3.086944 test-rmse:3.078922 [5] train-rmse:2.185500 test-rmse:2.222818 [6] train-rmse:1.562129 test-rmse:1.650350 [7] train-rmse:1.134930 test-rmse:1.291164 [8] train-rmse:0.848488 test-rmse:1.073852 [9] train-rmse:0.662502 test-rmse:0.949098 [10] train-rmse:0.538312 test-rmse:0.882958 [11] train-rmse:0.465128 test-rmse:0.849650 [12] train-rmse:0.419823 test-rmse:0.833703 [13] train-rmse:0.391019 test-rmse:0.827557 [14] train-rmse:0.375160 test-rmse:0.826726 [15] train-rmse:0.363848 test-rmse:0.828122 [16] train-rmse:0.353655 test-rmse:0.828888 [17] train-rmse:0.350254 test-rmse:0.829449 Stopping. Best iteration: [14] train-rmse:0.375160 test-rmse:0.826726 [1] train-rmse:8.876709 test-rmse:8.924455 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.226289 test-rmse:6.285813 [3] train-rmse:4.373942 test-rmse:4.426543 [4] train-rmse:3.081197 test-rmse:3.148112 [5] train-rmse:2.181848 test-rmse:2.268747 [6] train-rmse:1.560097 test-rmse:1.678277 [7] train-rmse:1.135630 test-rmse:1.305380 [8] train-rmse:0.848164 test-rmse:1.078300 [9] train-rmse:0.661308 test-rmse:0.953916 [10] train-rmse:0.539950 test-rmse:0.893245 [11] train-rmse:0.463744 test-rmse:0.863886 [12] train-rmse:0.420754 test-rmse:0.855339 [13] train-rmse:0.391140 test-rmse:0.854315 [14] train-rmse:0.374461 test-rmse:0.854464 [15] train-rmse:0.366918 test-rmse:0.856538 [16] train-rmse:0.357816 test-rmse:0.862079 Stopping. Best iteration: [13] train-rmse:0.391140 test-rmse:0.854315 [1] "overall test rmse:"
#imp_table_bb = imp_table_bb[order(-avg_gain)]
#imp_table_bb[, .SD[1], .(borough,b_class_group)][order(-avg_gain)]
dt_tree = imp_table_bb_wo[,.(M = mean(avg_gain, na.rm = TRUE), N = .N) ,.(b_class_group,Feature)]
options(repr.plot.width = 8, repr.plot.height = 5, repr.plot.res = 200)
ggplot(dt_tree
, aes(area = M, fill = N, label = Feature,subgroup = b_class_group)) +
geom_treemap() +
geom_treemap_subgroup_border() +
geom_treemap_subgroup_text(place = "centre", grow = T, alpha = 0.5, colour =
"black", fontface = "italic", min.size = 0) +
geom_treemap_text(colour = "white", place = "topleft", reflow = T)
feature_list = c( "commercialunits_group","residentialunits_group","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
,"grosssquarefeet_log_filled")
train_target = "saleprice_log_wo"
test_target = "saleprice_log"
fit_bb_wo = model_xgboost_partial_wo(feature_list,train_target,test_target,chunk_no = 5)
pred_table_bb_wo = fit_bb_wo[[1]]
imp_table_bb_wo = fit_bb_wo[[2]]
print("overall test rmse:")
calc_rmse(pred_table_bb_wo$pred,pred_table_bb_wo$actual)
calc_rmse(pred_table_bb_wo[actual < 20000000]$pred,pred_table_bb_wo[actual < 20000000]$actual)
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.552166 test-rmse:9.356132 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.722876 test-rmse:6.622382 [3] train-rmse:4.749852 test-rmse:4.720185 [4] train-rmse:3.381752 test-rmse:3.395814 [5] train-rmse:2.441664 test-rmse:2.496826 [6] train-rmse:1.807342 test-rmse:1.879660 [7] train-rmse:1.386907 test-rmse:1.469850 [8] train-rmse:1.119834 test-rmse:1.197467 [9] train-rmse:0.951159 test-rmse:1.040259 [10] train-rmse:0.851489 test-rmse:0.944681 [11] train-rmse:0.796225 test-rmse:0.887285 [12] train-rmse:0.765651 test-rmse:0.855002 [13] train-rmse:0.748954 test-rmse:0.832994 [14] train-rmse:0.738865 test-rmse:0.822012 [15] train-rmse:0.730175 test-rmse:0.818858 [16] train-rmse:0.725509 test-rmse:0.814039 [17] train-rmse:0.722236 test-rmse:0.811017 [18] train-rmse:0.717391 test-rmse:0.809390 [19] train-rmse:0.715096 test-rmse:0.807500 [20] train-rmse:0.708095 test-rmse:0.806905 [1] train-rmse:9.478835 test-rmse:9.564686 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.667587 test-rmse:6.718262 [3] train-rmse:4.708573 test-rmse:4.744218 [4] train-rmse:3.349213 test-rmse:3.409003 [5] train-rmse:2.415107 test-rmse:2.510697 [6] train-rmse:1.783584 test-rmse:1.911786 [7] train-rmse:1.366583 test-rmse:1.526169 [8] train-rmse:1.098492 test-rmse:1.290306 [9] train-rmse:0.933654 test-rmse:1.146358 [10] train-rmse:0.834618 test-rmse:1.072713 [11] train-rmse:0.782863 test-rmse:1.022738 [12] train-rmse:0.747495 test-rmse:0.991923 [13] train-rmse:0.726673 test-rmse:0.972902 [14] train-rmse:0.716719 test-rmse:0.962854 [15] train-rmse:0.710636 test-rmse:0.954097 [16] train-rmse:0.704451 test-rmse:0.943117 [17] train-rmse:0.699359 test-rmse:0.941115 [18] train-rmse:0.694064 test-rmse:0.938935 [19] train-rmse:0.692380 test-rmse:0.941584 [20] train-rmse:0.691748 test-rmse:0.941569 [1] train-rmse:9.441979 test-rmse:9.616797 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.641438 test-rmse:6.672493 [3] train-rmse:4.687449 test-rmse:4.643723 [4] train-rmse:3.329764 test-rmse:3.240144 [5] train-rmse:2.395095 test-rmse:2.317721 [6] train-rmse:1.760815 test-rmse:1.739120 [7] train-rmse:1.339955 test-rmse:1.410253 [8] train-rmse:1.071505 test-rmse:1.268230 [9] train-rmse:0.904835 test-rmse:1.223678 [10] train-rmse:0.808494 test-rmse:1.221457 [11] train-rmse:0.750516 test-rmse:1.239176 [12] train-rmse:0.719517 test-rmse:1.256272 [13] train-rmse:0.701411 test-rmse:1.277213 Stopping. Best iteration: [10] train-rmse:0.808494 test-rmse:1.221457 [1] train-rmse:9.523540 test-rmse:9.453149 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.703382 test-rmse:6.661200 [3] train-rmse:4.737344 test-rmse:4.711920 [4] train-rmse:3.373898 test-rmse:3.359253 [5] train-rmse:2.437145 test-rmse:2.439354 [6] train-rmse:1.800031 test-rmse:1.814345 [7] train-rmse:1.377536 test-rmse:1.395605 [8] train-rmse:1.115309 test-rmse:1.122151 [9] train-rmse:0.953051 test-rmse:0.955893 [10] train-rmse:0.853201 test-rmse:0.869186 [11] train-rmse:0.797842 test-rmse:0.816307 [12] train-rmse:0.768042 test-rmse:0.786522 [13] train-rmse:0.751721 test-rmse:0.769124 [14] train-rmse:0.740864 test-rmse:0.759722 [15] train-rmse:0.727408 test-rmse:0.753672 [16] train-rmse:0.719280 test-rmse:0.754076 [17] train-rmse:0.716694 test-rmse:0.753506 [18] train-rmse:0.715035 test-rmse:0.753742 [19] train-rmse:0.711819 test-rmse:0.752802 [20] train-rmse:0.708535 test-rmse:0.752551 [1] train-rmse:9.512008 test-rmse:9.501357 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.694768 test-rmse:6.705690 [3] train-rmse:4.730522 test-rmse:4.751622 [4] train-rmse:3.368159 test-rmse:3.395847 [5] train-rmse:2.432105 test-rmse:2.494572 [6] train-rmse:1.793460 test-rmse:1.876423 [7] train-rmse:1.374566 test-rmse:1.473875 [8] train-rmse:1.107433 test-rmse:1.214419 [9] train-rmse:0.940074 test-rmse:1.056756 [10] train-rmse:0.836632 test-rmse:0.962900 [11] train-rmse:0.778241 test-rmse:0.902757 [12] train-rmse:0.745838 test-rmse:0.870008 [13] train-rmse:0.725648 test-rmse:0.850070 [14] train-rmse:0.715363 test-rmse:0.839376 [15] train-rmse:0.710336 test-rmse:0.831481 [16] train-rmse:0.707590 test-rmse:0.826856 [17] train-rmse:0.697620 test-rmse:0.829502 [18] train-rmse:0.693364 test-rmse:0.832431 [19] train-rmse:0.691587 test-rmse:0.832741 Stopping. Best iteration: [16] train-rmse:0.707590 test-rmse:0.826856
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.308808 test-rmse:9.383113 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.544896 test-rmse:6.614523 [3] train-rmse:4.619780 test-rmse:4.701146 [4] train-rmse:3.286172 test-rmse:3.376243 [5] train-rmse:2.371560 test-rmse:2.466408 [6] train-rmse:1.755249 test-rmse:1.865244 [7] train-rmse:1.353386 test-rmse:1.478029 [8] train-rmse:1.103126 test-rmse:1.233209 [9] train-rmse:0.955495 test-rmse:1.080280 [10] train-rmse:0.873833 test-rmse:0.996453 [11] train-rmse:0.830059 test-rmse:0.947693 [12] train-rmse:0.807304 test-rmse:0.922450 [13] train-rmse:0.795712 test-rmse:0.906488 [14] train-rmse:0.789243 test-rmse:0.899218 [15] train-rmse:0.786247 test-rmse:0.893770 [16] train-rmse:0.784595 test-rmse:0.890235 [17] train-rmse:0.783497 test-rmse:0.889535 [18] train-rmse:0.782865 test-rmse:0.887718 [19] train-rmse:0.782489 test-rmse:0.886368 [20] train-rmse:0.782222 test-rmse:0.885624 [1] train-rmse:9.363310 test-rmse:9.041458 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.582728 test-rmse:6.321845 [3] train-rmse:4.646228 test-rmse:4.427988 [4] train-rmse:3.304733 test-rmse:3.116728 [5] train-rmse:2.385146 test-rmse:2.218702 [6] train-rmse:1.764524 test-rmse:1.615517 [7] train-rmse:1.359308 test-rmse:1.230674 [8] train-rmse:1.106155 test-rmse:1.005940 [9] train-rmse:0.957862 test-rmse:0.887344 [10] train-rmse:0.874824 test-rmse:0.831154 [11] train-rmse:0.830340 test-rmse:0.808437 [12] train-rmse:0.806934 test-rmse:0.800758 [13] train-rmse:0.794590 test-rmse:0.800441 [14] train-rmse:0.788786 test-rmse:0.802855 [15] train-rmse:0.785363 test-rmse:0.805400 [16] train-rmse:0.783226 test-rmse:0.809256 Stopping. Best iteration: [13] train-rmse:0.794590 test-rmse:0.800441 [1] train-rmse:9.302868 test-rmse:9.416537 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.538085 test-rmse:6.672723 [3] train-rmse:4.611826 test-rmse:4.762771 [4] train-rmse:3.276238 test-rmse:3.442531 [5] train-rmse:2.359173 test-rmse:2.544188 [6] train-rmse:1.740110 test-rmse:1.944056 [7] train-rmse:1.332233 test-rmse:1.551278 [8] train-rmse:1.078034 test-rmse:1.304271 [9] train-rmse:0.925553 test-rmse:1.153505 [10] train-rmse:0.839409 test-rmse:1.063906 [11] train-rmse:0.793734 test-rmse:1.011504 [12] train-rmse:0.769499 test-rmse:0.980907 [13] train-rmse:0.756913 test-rmse:0.963395 [14] train-rmse:0.750434 test-rmse:0.952132 [15] train-rmse:0.747173 test-rmse:0.945183 [16] train-rmse:0.745374 test-rmse:0.941081 [17] train-rmse:0.744208 test-rmse:0.938102 [18] train-rmse:0.743694 test-rmse:0.936159 [19] train-rmse:0.743320 test-rmse:0.934835 [20] train-rmse:0.742619 test-rmse:0.935295 [1] train-rmse:9.301092 test-rmse:9.426648 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.538011 test-rmse:6.620306 [3] train-rmse:4.613751 test-rmse:4.668963 [4] train-rmse:3.280502 test-rmse:3.316126 [5] train-rmse:2.365936 test-rmse:2.392572 [6] train-rmse:1.749054 test-rmse:1.781061 [7] train-rmse:1.346325 test-rmse:1.389146 [8] train-rmse:1.094277 test-rmse:1.151284 [9] train-rmse:0.945719 test-rmse:1.017018 [10] train-rmse:0.862418 test-rmse:0.945231 [11] train-rmse:0.818385 test-rmse:0.910301 [12] train-rmse:0.795416 test-rmse:0.893795 [13] train-rmse:0.783486 test-rmse:0.885623 [14] train-rmse:0.777028 test-rmse:0.883574 [15] train-rmse:0.773008 test-rmse:0.883256 [16] train-rmse:0.771072 test-rmse:0.883242 [17] train-rmse:0.769584 test-rmse:0.884061 [18] train-rmse:0.769013 test-rmse:0.884893 [19] train-rmse:0.768049 test-rmse:0.885619 Stopping. Best iteration: [16] train-rmse:0.771072 test-rmse:0.883242 [1] train-rmse:9.315903 test-rmse:9.328956 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.549110 test-rmse:6.549677 [3] train-rmse:4.622064 test-rmse:4.615303 [4] train-rmse:3.287222 test-rmse:3.277448 [5] train-rmse:2.372351 test-rmse:2.363482 [6] train-rmse:1.755616 test-rmse:1.755793 [7] train-rmse:1.353293 test-rmse:1.359298 [8] train-rmse:1.101432 test-rmse:1.126114 [9] train-rmse:0.955145 test-rmse:0.990575 [10] train-rmse:0.873188 test-rmse:0.917285 [11] train-rmse:0.829655 test-rmse:0.883707 [12] train-rmse:0.806220 test-rmse:0.873463 [13] train-rmse:0.794828 test-rmse:0.865955 [14] train-rmse:0.788646 test-rmse:0.867232 [15] train-rmse:0.785494 test-rmse:0.867877 [16] train-rmse:0.783791 test-rmse:0.870873 Stopping. Best iteration: [13] train-rmse:0.794828 test-rmse:0.865955
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.674449 test-rmse:9.902641 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.805454 test-rmse:7.021844 [3] train-rmse:4.810263 test-rmse:5.016567 [4] train-rmse:3.430548 test-rmse:3.627504 [5] train-rmse:2.488140 test-rmse:2.678684 [6] train-rmse:1.858256 test-rmse:2.047536 [7] train-rmse:1.451247 test-rmse:1.635149 [8] train-rmse:1.201738 test-rmse:1.375239 [9] train-rmse:1.057748 test-rmse:1.212876 [10] train-rmse:0.978931 test-rmse:1.113530 [11] train-rmse:0.937587 test-rmse:1.053960 [12] train-rmse:0.916496 test-rmse:1.017289 [13] train-rmse:0.905871 test-rmse:0.992984 [14] train-rmse:0.900560 test-rmse:0.977546 [15] train-rmse:0.897845 test-rmse:0.968826 [16] train-rmse:0.896485 test-rmse:0.962047 [17] train-rmse:0.895762 test-rmse:0.958157 [18] train-rmse:0.895378 test-rmse:0.955633 [19] train-rmse:0.895196 test-rmse:0.953369 [20] train-rmse:0.895098 test-rmse:0.952091 [1] train-rmse:9.719570 test-rmse:9.634528 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.835660 test-rmse:6.751735 [3] train-rmse:4.829383 test-rmse:4.749918 [4] train-rmse:3.440607 test-rmse:3.371384 [5] train-rmse:2.490455 test-rmse:2.438999 [6] train-rmse:1.853656 test-rmse:1.812228 [7] train-rmse:1.440221 test-rmse:1.425579 [8] train-rmse:1.185173 test-rmse:1.204059 [9] train-rmse:1.037506 test-rmse:1.090248 [10] train-rmse:0.955734 test-rmse:1.037899 [11] train-rmse:0.912882 test-rmse:1.016971 [12] train-rmse:0.890931 test-rmse:1.010265 [13] train-rmse:0.879917 test-rmse:1.009464 [14] train-rmse:0.874398 test-rmse:1.010748 [15] train-rmse:0.871630 test-rmse:1.012513 [16] train-rmse:0.870144 test-rmse:1.014786 Stopping. Best iteration: [13] train-rmse:0.879917 test-rmse:1.009464 [1] train-rmse:9.733046 test-rmse:9.548749 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.845657 test-rmse:6.663014 [3] train-rmse:4.837121 test-rmse:4.661697 [4] train-rmse:3.448098 test-rmse:3.287019 [5] train-rmse:2.498920 test-rmse:2.360548 [6] train-rmse:1.863855 test-rmse:1.764114 [7] train-rmse:1.454089 test-rmse:1.402297 [8] train-rmse:1.201342 test-rmse:1.191730 [9] train-rmse:1.055262 test-rmse:1.089283 [10] train-rmse:0.975266 test-rmse:1.044113 [11] train-rmse:0.933323 test-rmse:1.035291 [12] train-rmse:0.911617 test-rmse:1.029937 [13] train-rmse:0.900765 test-rmse:1.035979 [14] train-rmse:0.895197 test-rmse:1.036587 [15] train-rmse:0.892482 test-rmse:1.039066 Stopping. Best iteration: [12] train-rmse:0.911617 test-rmse:1.029937 [1] train-rmse:9.685002 test-rmse:9.861870 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.813019 test-rmse:6.994197 [3] train-rmse:4.815794 test-rmse:5.002559 [4] train-rmse:3.434503 test-rmse:3.619825 [5] train-rmse:2.490833 test-rmse:2.677537 [6] train-rmse:1.858704 test-rmse:2.037955 [7] train-rmse:1.449869 test-rmse:1.618395 [8] train-rmse:1.199055 test-rmse:1.350785 [9] train-rmse:1.053912 test-rmse:1.186760 [10] train-rmse:0.974061 test-rmse:1.089179 [11] train-rmse:0.932530 test-rmse:1.031281 [12] train-rmse:0.911399 test-rmse:0.996620 [13] train-rmse:0.900184 test-rmse:0.977005 [14] train-rmse:0.894773 test-rmse:0.964820 [15] train-rmse:0.892056 test-rmse:0.958053 [16] train-rmse:0.890679 test-rmse:0.953349 [17] train-rmse:0.889796 test-rmse:0.951537 [18] train-rmse:0.889302 test-rmse:0.949981 [19] train-rmse:0.888939 test-rmse:0.949169 [20] train-rmse:0.888786 test-rmse:0.948600 [1] train-rmse:9.730931 test-rmse:9.580462 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.844924 test-rmse:6.712925 [3] train-rmse:4.837661 test-rmse:4.722809 [4] train-rmse:3.450302 test-rmse:3.341393 [5] train-rmse:2.500619 test-rmse:2.413474 [6] train-rmse:1.864977 test-rmse:1.804315 [7] train-rmse:1.454846 test-rmse:1.436407 [8] train-rmse:1.203320 test-rmse:1.227359 [9] train-rmse:1.057188 test-rmse:1.115142 [10] train-rmse:0.977567 test-rmse:1.063588 [11] train-rmse:0.935973 test-rmse:1.045870 [12] train-rmse:0.914655 test-rmse:1.041810 [13] train-rmse:0.903461 test-rmse:1.041507 [14] train-rmse:0.897450 test-rmse:1.043846 [15] train-rmse:0.894699 test-rmse:1.047775 [16] train-rmse:0.893180 test-rmse:1.047318 Stopping. Best iteration: [13] train-rmse:0.903461 test-rmse:1.041507
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:10.252530 test-rmse:11.562238 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.239427 test-rmse:8.371754 [3] train-rmse:5.133748 test-rmse:6.148461 [4] train-rmse:3.667107 test-rmse:4.615304 [5] train-rmse:2.650007 test-rmse:3.579392 [6] train-rmse:1.955148 test-rmse:2.865821 [7] train-rmse:1.474036 test-rmse:2.398241 [8] train-rmse:1.159802 test-rmse:2.082459 [9] train-rmse:0.936796 test-rmse:1.891940 [10] train-rmse:0.795431 test-rmse:1.753628 [11] train-rmse:0.704271 test-rmse:1.686116 [12] train-rmse:0.650744 test-rmse:1.630784 [13] train-rmse:0.619151 test-rmse:1.601396 [14] train-rmse:0.602428 test-rmse:1.583852 [15] train-rmse:0.580348 test-rmse:1.556586 [16] train-rmse:0.551703 test-rmse:1.548161 [17] train-rmse:0.546798 test-rmse:1.544354 [18] train-rmse:0.539324 test-rmse:1.535534 [19] train-rmse:0.536060 test-rmse:1.535850 [20] train-rmse:0.518573 test-rmse:1.528813 [1] train-rmse:10.314148 test-rmse:10.997541 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.281901 test-rmse:7.789572 [3] train-rmse:5.162544 test-rmse:5.556893 [4] train-rmse:3.688481 test-rmse:4.039722 [5] train-rmse:2.669882 test-rmse:3.009562 [6] train-rmse:1.964220 test-rmse:2.337490 [7] train-rmse:1.492273 test-rmse:1.912657 [8] train-rmse:1.167537 test-rmse:1.650182 [9] train-rmse:0.957663 test-rmse:1.494337 [10] train-rmse:0.816114 test-rmse:1.398553 [11] train-rmse:0.716734 test-rmse:1.365795 [12] train-rmse:0.657827 test-rmse:1.329828 [13] train-rmse:0.613318 test-rmse:1.309235 [14] train-rmse:0.591613 test-rmse:1.306056 [15] train-rmse:0.558745 test-rmse:1.307351 [16] train-rmse:0.537403 test-rmse:1.308720 [17] train-rmse:0.529744 test-rmse:1.313674 Stopping. Best iteration: [14] train-rmse:0.591613 test-rmse:1.306056 [1] train-rmse:10.315773 test-rmse:11.048160 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.284554 test-rmse:7.841098 [3] train-rmse:5.166527 test-rmse:5.610078 [4] train-rmse:3.695392 test-rmse:4.072908 [5] train-rmse:2.672482 test-rmse:3.061420 [6] train-rmse:1.974224 test-rmse:2.359581 [7] train-rmse:1.490956 test-rmse:1.928146 [8] train-rmse:1.167716 test-rmse:1.671370 [9] train-rmse:0.958065 test-rmse:1.531240 [10] train-rmse:0.808589 test-rmse:1.452255 [11] train-rmse:0.723693 test-rmse:1.386347 [12] train-rmse:0.659707 test-rmse:1.374084 [13] train-rmse:0.614509 test-rmse:1.343083 [14] train-rmse:0.587544 test-rmse:1.336159 [15] train-rmse:0.563929 test-rmse:1.326786 [16] train-rmse:0.558358 test-rmse:1.316317 [17] train-rmse:0.544734 test-rmse:1.326789 [18] train-rmse:0.533692 test-rmse:1.324026 [19] train-rmse:0.531512 test-rmse:1.319926 Stopping. Best iteration: [16] train-rmse:0.558358 test-rmse:1.316317 [1] train-rmse:10.842122 test-rmse:7.365639 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.653814 test-rmse:4.478764 [3] train-rmse:5.440280 test-rmse:2.906180 [4] train-rmse:3.891573 test-rmse:2.221582 [5] train-rmse:2.844885 test-rmse:1.989385 [6] train-rmse:2.131052 test-rmse:2.226874 [7] train-rmse:1.626742 test-rmse:1.816520 [8] train-rmse:1.280070 test-rmse:1.530395 [9] train-rmse:1.058060 test-rmse:1.332453 [10] train-rmse:0.914613 test-rmse:1.180745 [11] train-rmse:0.825052 test-rmse:1.256611 [12] train-rmse:0.764221 test-rmse:1.151829 [13] train-rmse:0.709432 test-rmse:1.074199 [14] train-rmse:0.691970 test-rmse:1.019982 [15] train-rmse:0.681932 test-rmse:1.031768 [16] train-rmse:0.661964 test-rmse:1.040049 [17] train-rmse:0.626302 test-rmse:1.034746 Stopping. Best iteration: [14] train-rmse:0.691970 test-rmse:1.019982 [1] train-rmse:10.321152 test-rmse:10.949098 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.285849 test-rmse:7.833587 [3] train-rmse:5.164058 test-rmse:5.668716 [4] train-rmse:3.689089 test-rmse:4.177852 [5] train-rmse:2.672146 test-rmse:3.145622 [6] train-rmse:1.969479 test-rmse:2.457702 [7] train-rmse:1.491680 test-rmse:2.017532 [8] train-rmse:1.169708 test-rmse:1.749189 [9] train-rmse:0.944854 test-rmse:1.605265 [10] train-rmse:0.806483 test-rmse:1.520516 [11] train-rmse:0.711503 test-rmse:1.483419 [12] train-rmse:0.661207 test-rmse:1.445966 [13] train-rmse:0.625132 test-rmse:1.439187 [14] train-rmse:0.608733 test-rmse:1.428466 [15] train-rmse:0.586604 test-rmse:1.424956 [16] train-rmse:0.580716 test-rmse:1.418136 [17] train-rmse:0.553123 test-rmse:1.426193 [18] train-rmse:0.542823 test-rmse:1.426481 [19] train-rmse:0.540380 test-rmse:1.424907 Stopping. Best iteration: [16] train-rmse:0.580716 test-rmse:1.418136
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:10.617598 test-rmse:10.605449 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.533655 test-rmse:7.515969 [3] train-rmse:5.377080 test-rmse:5.351726 [4] train-rmse:3.872694 test-rmse:3.920503 [5] train-rmse:2.838739 test-rmse:2.932069 [6] train-rmse:2.106102 test-rmse:2.248857 [7] train-rmse:1.559324 test-rmse:1.798706 [8] train-rmse:1.204557 test-rmse:1.516981 [9] train-rmse:0.927225 test-rmse:1.293522 [10] train-rmse:0.740821 test-rmse:1.138340 [11] train-rmse:0.617704 test-rmse:1.045821 [12] train-rmse:0.525444 test-rmse:0.980102 [13] train-rmse:0.443226 test-rmse:0.956574 [14] train-rmse:0.387649 test-rmse:0.936878 [15] train-rmse:0.343823 test-rmse:0.970338 [16] train-rmse:0.319253 test-rmse:0.958300 [17] train-rmse:0.297052 test-rmse:0.972906 Stopping. Best iteration: [14] train-rmse:0.387649 test-rmse:0.936878 [1] train-rmse:10.814026 test-rmse:9.228526 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.652288 test-rmse:6.085682 [3] train-rmse:5.433615 test-rmse:3.903089 [4] train-rmse:3.884176 test-rmse:2.428269 [5] train-rmse:2.807911 test-rmse:1.523705 [6] train-rmse:2.060585 test-rmse:1.115992 [7] train-rmse:1.552759 test-rmse:1.051362 [8] train-rmse:1.169018 test-rmse:1.146258 [9] train-rmse:0.902717 test-rmse:1.251198 [10] train-rmse:0.722418 test-rmse:1.347014 Stopping. Best iteration: [7] train-rmse:1.552759 test-rmse:1.051362 [1] train-rmse:10.537584 test-rmse:11.109623 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.470593 test-rmse:8.048811 [3] train-rmse:5.324022 test-rmse:5.908852 [4] train-rmse:3.826939 test-rmse:4.386625 [5] train-rmse:2.792569 test-rmse:3.363081 [6] train-rmse:2.050176 test-rmse:2.594953 [7] train-rmse:1.530243 test-rmse:2.073843 [8] train-rmse:1.156716 test-rmse:1.781751 [9] train-rmse:0.900255 test-rmse:1.613071 [10] train-rmse:0.728979 test-rmse:1.493328 [11] train-rmse:0.600196 test-rmse:1.422920 [12] train-rmse:0.516129 test-rmse:1.372547 [13] train-rmse:0.461053 test-rmse:1.356703 [14] train-rmse:0.413531 test-rmse:1.357909 [15] train-rmse:0.379781 test-rmse:1.352786 [16] train-rmse:0.353219 test-rmse:1.355351 [17] train-rmse:0.332878 test-rmse:1.362404 [18] train-rmse:0.314575 test-rmse:1.369283 Stopping. Best iteration: [15] train-rmse:0.379781 test-rmse:1.352786 [1] train-rmse:10.546200 test-rmse:11.042486 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.487599 test-rmse:7.957868 [3] train-rmse:5.351235 test-rmse:5.784976 [4] train-rmse:3.867234 test-rmse:4.218158 [5] train-rmse:2.849100 test-rmse:3.139201 [6] train-rmse:2.131590 test-rmse:2.330535 [7] train-rmse:1.590971 test-rmse:1.720061 [8] train-rmse:1.211898 test-rmse:1.293123 [9] train-rmse:0.948597 test-rmse:1.018695 [10] train-rmse:0.781194 test-rmse:0.878645 [11] train-rmse:0.666957 test-rmse:0.776282 [12] train-rmse:0.569950 test-rmse:0.721037 [13] train-rmse:0.523442 test-rmse:0.668373 [14] train-rmse:0.474842 test-rmse:0.635353 [15] train-rmse:0.442841 test-rmse:0.616325 [16] train-rmse:0.421337 test-rmse:0.609920 [17] train-rmse:0.400421 test-rmse:0.611100 [18] train-rmse:0.388284 test-rmse:0.609450 [19] train-rmse:0.371240 test-rmse:0.605704 [20] train-rmse:0.363710 test-rmse:0.602405 [1] train-rmse:10.548206 test-rmse:11.030745 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.489059 test-rmse:7.945662 [3] train-rmse:5.352331 test-rmse:5.772517 [4] train-rmse:3.868041 test-rmse:4.205568 [5] train-rmse:2.849757 test-rmse:3.126703 [6] train-rmse:2.132514 test-rmse:2.293198 [7] train-rmse:1.598782 test-rmse:1.688922 [8] train-rmse:1.250991 test-rmse:1.302978 [9] train-rmse:1.013772 test-rmse:0.979562 [10] train-rmse:0.819527 test-rmse:0.761963 [11] train-rmse:0.674700 test-rmse:0.662719 [12] train-rmse:0.576972 test-rmse:0.581397 [13] train-rmse:0.509555 test-rmse:0.589124 [14] train-rmse:0.458204 test-rmse:0.566656 [15] train-rmse:0.424107 test-rmse:0.585434 [16] train-rmse:0.395317 test-rmse:0.578552 [17] train-rmse:0.378625 test-rmse:0.569528 Stopping. Best iteration: [14] train-rmse:0.458204 test-rmse:0.566656
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:10.200396 test-rmse:10.714079 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.233291 test-rmse:7.734806 [3] train-rmse:5.154616 test-rmse:5.638537 [4] train-rmse:3.708273 test-rmse:4.166899 [5] train-rmse:2.707027 test-rmse:3.169872 [6] train-rmse:2.009138 test-rmse:2.457296 [7] train-rmse:1.512802 test-rmse:1.959589 [8] train-rmse:1.192783 test-rmse:1.619560 [9] train-rmse:0.990119 test-rmse:1.384874 [10] train-rmse:0.819867 test-rmse:1.263966 [11] train-rmse:0.702144 test-rmse:1.159311 [12] train-rmse:0.607270 test-rmse:1.094828 [13] train-rmse:0.557839 test-rmse:1.054695 [14] train-rmse:0.516943 test-rmse:1.040634 [15] train-rmse:0.462174 test-rmse:1.048087 [16] train-rmse:0.414967 test-rmse:1.046282 [17] train-rmse:0.398085 test-rmse:1.044443 Stopping. Best iteration: [14] train-rmse:0.516943 test-rmse:1.040634 [1] train-rmse:10.376463 test-rmse:9.621131 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.355875 test-rmse:6.586657 [3] train-rmse:5.239548 test-rmse:4.451729 [4] train-rmse:3.766570 test-rmse:2.954970 [5] train-rmse:2.731532 test-rmse:1.926690 [6] train-rmse:2.005709 test-rmse:1.300172 [7] train-rmse:1.513620 test-rmse:0.902932 [8] train-rmse:1.170534 test-rmse:0.718108 [9] train-rmse:0.946240 test-rmse:0.746768 [10] train-rmse:0.788438 test-rmse:0.812863 [11] train-rmse:0.670867 test-rmse:0.889947 Stopping. Best iteration: [8] train-rmse:1.170534 test-rmse:0.718108 [1] train-rmse:10.386724 test-rmse:9.457172 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.354051 test-rmse:6.460222 [3] train-rmse:5.225576 test-rmse:4.388751 [4] train-rmse:3.739174 test-rmse:2.995137 [5] train-rmse:2.709136 test-rmse:2.109943 [6] train-rmse:1.990750 test-rmse:1.623437 [7] train-rmse:1.500728 test-rmse:1.416058 [8] train-rmse:1.166450 test-rmse:1.366964 [9] train-rmse:0.950819 test-rmse:1.397099 [10] train-rmse:0.799598 test-rmse:1.428434 [11] train-rmse:0.700449 test-rmse:1.478524 Stopping. Best iteration: [8] train-rmse:1.166450 test-rmse:1.366964 [1] train-rmse:10.157991 test-rmse:10.985187 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.199400 test-rmse:8.015477 [3] train-rmse:5.125865 test-rmse:5.925558 [4] train-rmse:3.681809 test-rmse:4.457442 [5] train-rmse:2.688124 test-rmse:3.428999 [6] train-rmse:1.990703 test-rmse:2.679733 [7] train-rmse:1.519645 test-rmse:2.125153 [8] train-rmse:1.193395 test-rmse:1.741109 [9] train-rmse:0.996140 test-rmse:1.471713 [10] train-rmse:0.878074 test-rmse:1.282725 [11] train-rmse:0.777822 test-rmse:1.166874 [12] train-rmse:0.709488 test-rmse:1.049945 [13] train-rmse:0.664744 test-rmse:1.005604 [14] train-rmse:0.610196 test-rmse:1.026824 [15] train-rmse:0.562999 test-rmse:1.032735 [16] train-rmse:0.533745 test-rmse:1.059026 Stopping. Best iteration: [13] train-rmse:0.664744 test-rmse:1.005604 [1] train-rmse:10.251274 test-rmse:10.422952 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7.266932 test-rmse:7.436914 [3] train-rmse:5.175201 test-rmse:5.342409 [4] train-rmse:3.718461 test-rmse:3.881097 [5] train-rmse:2.716148 test-rmse:2.871352 [6] train-rmse:2.013842 test-rmse:2.168815 [7] train-rmse:1.537597 test-rmse:1.670234 [8] train-rmse:1.230992 test-rmse:1.352114 [9] train-rmse:1.003107 test-rmse:1.139765 [10] train-rmse:0.853983 test-rmse:1.012803 [11] train-rmse:0.743274 test-rmse:0.999966 [12] train-rmse:0.669721 test-rmse:1.017976 [13] train-rmse:0.618840 test-rmse:1.069102 [14] train-rmse:0.576155 test-rmse:1.119067 Stopping. Best iteration: [11] train-rmse:0.743274 test-rmse:0.999966
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.005816 test-rmse:9.060042 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.329307 test-rmse:6.392803 [3] train-rmse:4.457426 test-rmse:4.520964 [4] train-rmse:3.151157 test-rmse:3.224723 [5] train-rmse:2.243558 test-rmse:2.328691 [6] train-rmse:1.617224 test-rmse:1.721520 [7] train-rmse:1.194513 test-rmse:1.325256 [8] train-rmse:0.914457 test-rmse:1.075715 [9] train-rmse:0.728726 test-rmse:0.916603 [10] train-rmse:0.610119 test-rmse:0.832409 [11] train-rmse:0.543852 test-rmse:0.780429 [12] train-rmse:0.496690 test-rmse:0.748069 [13] train-rmse:0.469855 test-rmse:0.732988 [14] train-rmse:0.443138 test-rmse:0.726540 [15] train-rmse:0.429914 test-rmse:0.721325 [16] train-rmse:0.417888 test-rmse:0.721072 [17] train-rmse:0.410208 test-rmse:0.719363 [18] train-rmse:0.399264 test-rmse:0.717019 [19] train-rmse:0.393779 test-rmse:0.716466 [20] train-rmse:0.389107 test-rmse:0.715964 [1] train-rmse:9.012336 test-rmse:8.962632 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.334097 test-rmse:6.301795 [3] train-rmse:4.461183 test-rmse:4.466663 [4] train-rmse:3.154772 test-rmse:3.185925 [5] train-rmse:2.246598 test-rmse:2.309330 [6] train-rmse:1.619920 test-rmse:1.744270 [7] train-rmse:1.195086 test-rmse:1.377592 [8] train-rmse:0.909817 test-rmse:1.169014 [9] train-rmse:0.726147 test-rmse:1.060162 [10] train-rmse:0.609216 test-rmse:1.014781 [11] train-rmse:0.539816 test-rmse:1.002386 [12] train-rmse:0.486243 test-rmse:1.000049 [13] train-rmse:0.462405 test-rmse:1.002735 [14] train-rmse:0.443620 test-rmse:1.010049 [15] train-rmse:0.427853 test-rmse:1.015040 Stopping. Best iteration: [12] train-rmse:0.486243 test-rmse:1.000049 [1] train-rmse:8.993429 test-rmse:9.061958 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.322291 test-rmse:6.376548 [3] train-rmse:4.455416 test-rmse:4.498289 [4] train-rmse:3.152214 test-rmse:3.239403 [5] train-rmse:2.248213 test-rmse:2.344149 [6] train-rmse:1.625952 test-rmse:1.774305 [7] train-rmse:1.203265 test-rmse:1.398339 [8] train-rmse:0.923613 test-rmse:1.155300 [9] train-rmse:0.743508 test-rmse:1.010542 [10] train-rmse:0.626307 test-rmse:0.948591 [11] train-rmse:0.561005 test-rmse:0.928460 [12] train-rmse:0.512173 test-rmse:0.919445 [13] train-rmse:0.483417 test-rmse:0.918023 [14] train-rmse:0.467322 test-rmse:0.917530 [15] train-rmse:0.452665 test-rmse:0.917960 [16] train-rmse:0.444318 test-rmse:0.914366 [17] train-rmse:0.426207 test-rmse:0.926632 [18] train-rmse:0.415671 test-rmse:0.929982 [19] train-rmse:0.406519 test-rmse:0.936289 Stopping. Best iteration: [16] train-rmse:0.444318 test-rmse:0.914366 [1] train-rmse:9.013486 test-rmse:9.013264 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.334933 test-rmse:6.360031 [3] train-rmse:4.461701 test-rmse:4.508011 [4] train-rmse:3.153378 test-rmse:3.218155 [5] train-rmse:2.244022 test-rmse:2.339933 [6] train-rmse:1.615656 test-rmse:1.741948 [7] train-rmse:1.188431 test-rmse:1.368388 [8] train-rmse:0.904375 test-rmse:1.109933 [9] train-rmse:0.713876 test-rmse:0.965246 [10] train-rmse:0.594460 test-rmse:0.891915 [11] train-rmse:0.523347 test-rmse:0.836199 [12] train-rmse:0.482114 test-rmse:0.805515 [13] train-rmse:0.458759 test-rmse:0.787488 [14] train-rmse:0.434234 test-rmse:0.783903 [15] train-rmse:0.425930 test-rmse:0.780834 [16] train-rmse:0.412754 test-rmse:0.777914 [17] train-rmse:0.400955 test-rmse:0.775498 [18] train-rmse:0.393209 test-rmse:0.784768 [19] train-rmse:0.389049 test-rmse:0.784528 [20] train-rmse:0.380841 test-rmse:0.785747 Stopping. Best iteration: [17] train-rmse:0.400955 test-rmse:0.775498 [1] train-rmse:9.017771 test-rmse:8.975226 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.339009 test-rmse:6.312791 [3] train-rmse:4.465899 test-rmse:4.465977 [4] train-rmse:3.158694 test-rmse:3.182086 [5] train-rmse:2.250326 test-rmse:2.298997 [6] train-rmse:1.626539 test-rmse:1.699949 [7] train-rmse:1.201344 test-rmse:1.307763 [8] train-rmse:0.919188 test-rmse:1.060065 [9] train-rmse:0.741249 test-rmse:0.919097 [10] train-rmse:0.630192 test-rmse:0.841028 [11] train-rmse:0.560391 test-rmse:0.800657 [12] train-rmse:0.517203 test-rmse:0.781519 [13] train-rmse:0.487821 test-rmse:0.779700 [14] train-rmse:0.461439 test-rmse:0.779390 [15] train-rmse:0.450670 test-rmse:0.778244 [16] train-rmse:0.441195 test-rmse:0.780851 [17] train-rmse:0.432489 test-rmse:0.785508 [18] train-rmse:0.423861 test-rmse:0.787940 Stopping. Best iteration: [15] train-rmse:0.450670 test-rmse:0.778244
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.308707 test-rmse:7.995579 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.843086 test-rmse:5.509439 [3] train-rmse:4.120651 test-rmse:3.878995 [4] train-rmse:2.922191 test-rmse:2.754561 [5] train-rmse:2.092083 test-rmse:2.002943 [6] train-rmse:1.525307 test-rmse:1.496307 [7] train-rmse:1.143842 test-rmse:1.174203 [8] train-rmse:0.898518 test-rmse:0.998257 [9] train-rmse:0.744950 test-rmse:0.908237 [10] train-rmse:0.656204 test-rmse:0.877039 [11] train-rmse:0.605564 test-rmse:0.869225 [12] train-rmse:0.578492 test-rmse:0.871097 [13] train-rmse:0.564437 test-rmse:0.876734 [14] train-rmse:0.557169 test-rmse:0.881866 Stopping. Best iteration: [11] train-rmse:0.605564 test-rmse:0.869225 [1] train-rmse:8.242745 test-rmse:8.464622 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.800102 test-rmse:5.980728 [3] train-rmse:4.095221 test-rmse:4.246429 [4] train-rmse:2.908763 test-rmse:3.077949 [5] train-rmse:2.088888 test-rmse:2.253081 [6] train-rmse:1.529105 test-rmse:1.748779 [7] train-rmse:1.152781 test-rmse:1.439860 [8] train-rmse:0.911334 test-rmse:1.214072 [9] train-rmse:0.764996 test-rmse:1.084562 [10] train-rmse:0.677662 test-rmse:1.036715 [11] train-rmse:0.629573 test-rmse:1.016298 [12] train-rmse:0.603856 test-rmse:1.008573 [13] train-rmse:0.590352 test-rmse:1.006880 [14] train-rmse:0.583306 test-rmse:1.006036 [15] train-rmse:0.579622 test-rmse:1.006619 [16] train-rmse:0.577683 test-rmse:1.007785 [17] train-rmse:0.576648 test-rmse:1.009218 Stopping. Best iteration: [14] train-rmse:0.583306 test-rmse:1.006036 [1] train-rmse:8.251737 test-rmse:8.343327 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.804507 test-rmse:5.858419 [3] train-rmse:4.095828 test-rmse:4.125178 [4] train-rmse:2.906269 test-rmse:2.920673 [5] train-rmse:2.085621 test-rmse:2.092655 [6] train-rmse:1.522563 test-rmse:1.532368 [7] train-rmse:1.140970 test-rmse:1.167713 [8] train-rmse:0.896928 test-rmse:0.935785 [9] train-rmse:0.741180 test-rmse:0.799955 [10] train-rmse:0.649277 test-rmse:0.724357 [11] train-rmse:0.596313 test-rmse:0.683841 [12] train-rmse:0.567513 test-rmse:0.664359 [13] train-rmse:0.552027 test-rmse:0.654140 [14] train-rmse:0.543734 test-rmse:0.649496 [15] train-rmse:0.539363 test-rmse:0.647088 [16] train-rmse:0.536934 test-rmse:0.646042 [17] train-rmse:0.535354 test-rmse:0.645413 [18] train-rmse:0.534733 test-rmse:0.645206 [19] train-rmse:0.534313 test-rmse:0.645143 [20] train-rmse:0.533934 test-rmse:0.645131 [1] train-rmse:8.217504 test-rmse:8.540228 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.780064 test-rmse:6.066843 [3] train-rmse:4.077964 test-rmse:4.340991 [4] train-rmse:2.892839 test-rmse:3.167921 [5] train-rmse:2.074936 test-rmse:2.332174 [6] train-rmse:1.513705 test-rmse:1.762180 [7] train-rmse:1.142114 test-rmse:1.381639 [8] train-rmse:0.903275 test-rmse:1.133792 [9] train-rmse:0.750230 test-rmse:0.981613 [10] train-rmse:0.662419 test-rmse:0.886868 [11] train-rmse:0.613572 test-rmse:0.825220 [12] train-rmse:0.587714 test-rmse:0.786112 [13] train-rmse:0.573193 test-rmse:0.764376 [14] train-rmse:0.565689 test-rmse:0.749858 [15] train-rmse:0.561668 test-rmse:0.741590 [16] train-rmse:0.559172 test-rmse:0.735300 [17] train-rmse:0.557102 test-rmse:0.732750 [18] train-rmse:0.556213 test-rmse:0.730079 [19] train-rmse:0.555173 test-rmse:0.730034 [20] train-rmse:0.554485 test-rmse:0.730601 [1] train-rmse:8.272318 test-rmse:8.236524 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.819273 test-rmse:5.834726 [3] train-rmse:4.106527 test-rmse:4.130485 [4] train-rmse:2.914913 test-rmse:2.976914 [5] train-rmse:2.093338 test-rmse:2.184366 [6] train-rmse:1.529576 test-rmse:1.635344 [7] train-rmse:1.157591 test-rmse:1.274873 [8] train-rmse:0.914707 test-rmse:1.048820 [9] train-rmse:0.760898 test-rmse:0.900018 [10] train-rmse:0.672188 test-rmse:0.821769 [11] train-rmse:0.622269 test-rmse:0.774862 [12] train-rmse:0.594680 test-rmse:0.752310 [13] train-rmse:0.580265 test-rmse:0.739598 [14] train-rmse:0.571029 test-rmse:0.734906 [15] train-rmse:0.566174 test-rmse:0.733941 [16] train-rmse:0.563397 test-rmse:0.734288 [17] train-rmse:0.561703 test-rmse:0.735999 [18] train-rmse:0.560893 test-rmse:0.735497 Stopping. Best iteration: [15] train-rmse:0.566174 test-rmse:0.733941
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.166001 test-rmse:8.608320 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.748271 test-rmse:6.198362 [3] train-rmse:4.062243 test-rmse:4.529066 [4] train-rmse:2.888964 test-rmse:3.405555 [5] train-rmse:2.073884 test-rmse:2.545495 [6] train-rmse:1.512235 test-rmse:1.966244 [7] train-rmse:1.131895 test-rmse:1.598206 [8] train-rmse:0.881856 test-rmse:1.359022 [9] train-rmse:0.723495 test-rmse:1.194471 [10] train-rmse:0.628298 test-rmse:1.099915 [11] train-rmse:0.573717 test-rmse:1.042430 [12] train-rmse:0.543645 test-rmse:1.002382 [13] train-rmse:0.527432 test-rmse:0.978656 [14] train-rmse:0.518847 test-rmse:0.959933 [15] train-rmse:0.514286 test-rmse:0.949604 [16] train-rmse:0.511878 test-rmse:0.942401 [17] train-rmse:0.510600 test-rmse:0.937224 [18] train-rmse:0.509914 test-rmse:0.933092 [19] train-rmse:0.509539 test-rmse:0.930701 [20] train-rmse:0.509334 test-rmse:0.928434 [1] train-rmse:8.282217 test-rmse:7.886954 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.833778 test-rmse:5.440467 [3] train-rmse:4.129923 test-rmse:3.740772 [4] train-rmse:2.942426 test-rmse:2.674646 [5] train-rmse:2.117773 test-rmse:1.943925 [6] train-rmse:1.552455 test-rmse:1.454571 [7] train-rmse:1.173406 test-rmse:1.139346 [8] train-rmse:0.927962 test-rmse:0.947405 [9] train-rmse:0.764563 test-rmse:0.835355 [10] train-rmse:0.664530 test-rmse:0.776158 [11] train-rmse:0.601874 test-rmse:0.747730 [12] train-rmse:0.564741 test-rmse:0.738982 [13] train-rmse:0.543719 test-rmse:0.738376 [14] train-rmse:0.531898 test-rmse:0.741300 [15] train-rmse:0.525207 test-rmse:0.745494 [16] train-rmse:0.521378 test-rmse:0.749911 Stopping. Best iteration: [13] train-rmse:0.543719 test-rmse:0.738376 [1] train-rmse:8.308361 test-rmse:7.727080 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.853351 test-rmse:5.266701 [3] train-rmse:4.145380 test-rmse:3.552643 [4] train-rmse:2.955384 test-rmse:2.490596 [5] train-rmse:2.129618 test-rmse:1.764074 [6] train-rmse:1.564419 test-rmse:1.279918 [7] train-rmse:1.186571 test-rmse:0.972759 [8] train-rmse:0.933129 test-rmse:0.793203 [9] train-rmse:0.770231 test-rmse:0.699695 [10] train-rmse:0.671587 test-rmse:0.657588 [11] train-rmse:0.614565 test-rmse:0.642186 [12] train-rmse:0.582677 test-rmse:0.638321 [13] train-rmse:0.565274 test-rmse:0.639444 [14] train-rmse:0.555878 test-rmse:0.642096 [15] train-rmse:0.550828 test-rmse:0.644893 Stopping. Best iteration: [12] train-rmse:0.582677 test-rmse:0.638321 [1] train-rmse:8.265890 test-rmse:7.993636 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.822973 test-rmse:5.551984 [3] train-rmse:4.123245 test-rmse:3.854966 [4] train-rmse:2.939322 test-rmse:2.679738 [5] train-rmse:2.117262 test-rmse:1.876514 [6] train-rmse:1.553850 test-rmse:1.335396 [7] train-rmse:1.176642 test-rmse:0.998762 [8] train-rmse:0.922510 test-rmse:0.801633 [9] train-rmse:0.758257 test-rmse:0.709603 [10] train-rmse:0.657969 test-rmse:0.676953 [11] train-rmse:0.599564 test-rmse:0.672989 [12] train-rmse:0.566814 test-rmse:0.679691 [13] train-rmse:0.548882 test-rmse:0.689663 [14] train-rmse:0.539199 test-rmse:0.699016 Stopping. Best iteration: [11] train-rmse:0.599564 test-rmse:0.672989 [1] train-rmse:8.127624 test-rmse:8.814696 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.715820 test-rmse:6.425705 [3] train-rmse:4.033691 test-rmse:4.772013 [4] train-rmse:2.861992 test-rmse:3.520267 [5] train-rmse:2.045712 test-rmse:2.655784 [6] train-rmse:1.480725 test-rmse:2.079645 [7] train-rmse:1.092692 test-rmse:1.711379 [8] train-rmse:0.825670 test-rmse:1.467621 [9] train-rmse:0.648837 test-rmse:1.327425 [10] train-rmse:0.535745 test-rmse:1.255192 [11] train-rmse:0.468164 test-rmse:1.217194 [12] train-rmse:0.426495 test-rmse:1.200094 [13] train-rmse:0.402556 test-rmse:1.193322 [14] train-rmse:0.390171 test-rmse:1.189356 [15] train-rmse:0.381864 test-rmse:1.187709 [16] train-rmse:0.376978 test-rmse:1.186879 [17] train-rmse:0.374058 test-rmse:1.188728 [18] train-rmse:0.372265 test-rmse:1.188098 [19] train-rmse:0.371148 test-rmse:1.187565 Stopping. Best iteration: [16] train-rmse:0.376978 test-rmse:1.186879
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.252199 test-rmse:9.289010 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.536190 test-rmse:6.580436 [3] train-rmse:4.644800 test-rmse:4.688581 [4] train-rmse:3.332635 test-rmse:3.416652 [5] train-rmse:2.435065 test-rmse:2.550634 [6] train-rmse:1.831626 test-rmse:1.998295 [7] train-rmse:1.425748 test-rmse:1.665850 [8] train-rmse:1.167130 test-rmse:1.470860 [9] train-rmse:1.009456 test-rmse:1.364891 [10] train-rmse:0.916961 test-rmse:1.307733 [11] train-rmse:0.861930 test-rmse:1.277039 [12] train-rmse:0.829841 test-rmse:1.264793 [13] train-rmse:0.810885 test-rmse:1.255990 [14] train-rmse:0.799564 test-rmse:1.253478 [15] train-rmse:0.786510 test-rmse:1.255457 [16] train-rmse:0.781892 test-rmse:1.254815 [17] train-rmse:0.769402 test-rmse:1.257042 Stopping. Best iteration: [14] train-rmse:0.799564 test-rmse:1.253478 [1] train-rmse:9.231738 test-rmse:9.435885 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.520951 test-rmse:6.717286 [3] train-rmse:4.633475 test-rmse:4.846131 [4] train-rmse:3.324351 test-rmse:3.533014 [5] train-rmse:2.428054 test-rmse:2.632276 [6] train-rmse:1.818544 test-rmse:2.086706 [7] train-rmse:1.418516 test-rmse:1.725430 [8] train-rmse:1.155092 test-rmse:1.504171 [9] train-rmse:0.993282 test-rmse:1.372630 [10] train-rmse:0.893104 test-rmse:1.300159 [11] train-rmse:0.831940 test-rmse:1.265445 [12] train-rmse:0.796855 test-rmse:1.247740 [13] train-rmse:0.779826 test-rmse:1.230137 [14] train-rmse:0.767127 test-rmse:1.223862 [15] train-rmse:0.751188 test-rmse:1.221800 [16] train-rmse:0.746411 test-rmse:1.217927 [17] train-rmse:0.738304 test-rmse:1.216011 [18] train-rmse:0.724494 test-rmse:1.215283 [19] train-rmse:0.723334 test-rmse:1.214036 [20] train-rmse:0.717443 test-rmse:1.214959 [1] train-rmse:9.279931 test-rmse:9.167756 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.557322 test-rmse:6.502502 [3] train-rmse:4.662938 test-rmse:4.662268 [4] train-rmse:3.352121 test-rmse:3.396408 [5] train-rmse:2.457377 test-rmse:2.550357 [6] train-rmse:1.856938 test-rmse:1.993968 [7] train-rmse:1.457965 test-rmse:1.650722 [8] train-rmse:1.202141 test-rmse:1.435138 [9] train-rmse:1.046818 test-rmse:1.268347 [10] train-rmse:0.954992 test-rmse:1.178586 [11] train-rmse:0.899374 test-rmse:1.132090 [12] train-rmse:0.869830 test-rmse:1.110668 [13] train-rmse:0.843082 test-rmse:1.105189 [14] train-rmse:0.831278 test-rmse:1.106816 [15] train-rmse:0.824640 test-rmse:1.111762 [16] train-rmse:0.815274 test-rmse:1.116142 Stopping. Best iteration: [13] train-rmse:0.843082 test-rmse:1.105189 [1] train-rmse:9.172651 test-rmse:9.867160 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.484277 test-rmse:7.077303 [3] train-rmse:4.614341 test-rmse:5.165431 [4] train-rmse:3.318621 test-rmse:3.782095 [5] train-rmse:2.431775 test-rmse:2.847249 [6] train-rmse:1.833993 test-rmse:2.222638 [7] train-rmse:1.437777 test-rmse:1.814088 [8] train-rmse:1.185163 test-rmse:1.533335 [9] train-rmse:1.028345 test-rmse:1.344746 [10] train-rmse:0.938251 test-rmse:1.231742 [11] train-rmse:0.884821 test-rmse:1.158732 [12] train-rmse:0.852558 test-rmse:1.108272 [13] train-rmse:0.828343 test-rmse:1.085333 [14] train-rmse:0.813616 test-rmse:1.085149 [15] train-rmse:0.801520 test-rmse:1.068728 [16] train-rmse:0.793865 test-rmse:1.061052 [17] train-rmse:0.782201 test-rmse:1.055953 [18] train-rmse:0.775493 test-rmse:1.056127 [19] train-rmse:0.768336 test-rmse:1.062840 [20] train-rmse:0.766565 test-rmse:1.058176 Stopping. Best iteration: [17] train-rmse:0.782201 test-rmse:1.055953 [1] train-rmse:9.327338 test-rmse:8.813021 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.584177 test-rmse:6.176066 [3] train-rmse:4.671837 test-rmse:4.375684 [4] train-rmse:3.347593 test-rmse:3.155365 [5] train-rmse:2.437581 test-rmse:2.393965 [6] train-rmse:1.823347 test-rmse:1.942253 [7] train-rmse:1.417165 test-rmse:1.697551 [8] train-rmse:1.149528 test-rmse:1.590104 [9] train-rmse:0.978474 test-rmse:1.553489 [10] train-rmse:0.874809 test-rmse:1.546740 [11] train-rmse:0.805803 test-rmse:1.547475 [12] train-rmse:0.766760 test-rmse:1.556044 [13] train-rmse:0.739831 test-rmse:1.562413 Stopping. Best iteration: [10] train-rmse:0.874809 test-rmse:1.546740
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.711581 test-rmse:8.363816 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.114823 test-rmse:5.782295 [3] train-rmse:4.301817 test-rmse:3.992208 [4] train-rmse:3.038244 test-rmse:2.780060 [5] train-rmse:2.159998 test-rmse:1.973483 [6] train-rmse:1.554568 test-rmse:1.468588 [7] train-rmse:1.141839 test-rmse:1.165022 [8] train-rmse:0.862465 test-rmse:1.015246 [9] train-rmse:0.677877 test-rmse:0.950480 [10] train-rmse:0.563831 test-rmse:0.928897 [11] train-rmse:0.493226 test-rmse:0.929525 [12] train-rmse:0.452953 test-rmse:0.937181 [13] train-rmse:0.429596 test-rmse:0.943215 Stopping. Best iteration: [10] train-rmse:0.563831 test-rmse:0.928897 [1] train-rmse:8.640803 test-rmse:8.827027 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.065664 test-rmse:6.256505 [3] train-rmse:4.267974 test-rmse:4.464964 [4] train-rmse:3.017172 test-rmse:3.222034 [5] train-rmse:2.149648 test-rmse:2.361673 [6] train-rmse:1.552950 test-rmse:1.761148 [7] train-rmse:1.147177 test-rmse:1.355146 [8] train-rmse:0.875951 test-rmse:1.078689 [9] train-rmse:0.698093 test-rmse:0.913846 [10] train-rmse:0.587460 test-rmse:0.813612 [11] train-rmse:0.519647 test-rmse:0.757929 [12] train-rmse:0.476268 test-rmse:0.725632 [13] train-rmse:0.450470 test-rmse:0.708750 [14] train-rmse:0.432867 test-rmse:0.711055 [15] train-rmse:0.419133 test-rmse:0.708267 [16] train-rmse:0.412354 test-rmse:0.713947 [17] train-rmse:0.399931 test-rmse:0.709308 [18] train-rmse:0.391179 test-rmse:0.717707 Stopping. Best iteration: [15] train-rmse:0.419133 test-rmse:0.708267 [1] train-rmse:8.646050 test-rmse:8.806735 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.070551 test-rmse:6.229820 [3] train-rmse:4.271755 test-rmse:4.419957 [4] train-rmse:3.018053 test-rmse:3.167682 [5] train-rmse:2.148113 test-rmse:2.294449 [6] train-rmse:1.547597 test-rmse:1.692533 [7] train-rmse:1.141797 test-rmse:1.291214 [8] train-rmse:0.867581 test-rmse:1.025283 [9] train-rmse:0.690232 test-rmse:0.864899 [10] train-rmse:0.581169 test-rmse:0.768723 [11] train-rmse:0.514679 test-rmse:0.712714 [12] train-rmse:0.473829 test-rmse:0.684990 [13] train-rmse:0.450813 test-rmse:0.668206 [14] train-rmse:0.433114 test-rmse:0.662021 [15] train-rmse:0.426451 test-rmse:0.656530 [16] train-rmse:0.421363 test-rmse:0.653497 [17] train-rmse:0.418172 test-rmse:0.651204 [18] train-rmse:0.412780 test-rmse:0.654256 [19] train-rmse:0.411115 test-rmse:0.653587 [20] train-rmse:0.402592 test-rmse:0.655526 Stopping. Best iteration: [17] train-rmse:0.418172 test-rmse:0.651204 [1] train-rmse:8.654594 test-rmse:8.745791 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.074770 test-rmse:6.169296 [3] train-rmse:4.273568 test-rmse:4.372638 [4] train-rmse:3.019985 test-rmse:3.125094 [5] train-rmse:2.149606 test-rmse:2.265499 [6] train-rmse:1.549970 test-rmse:1.674521 [7] train-rmse:1.144444 test-rmse:1.280963 [8] train-rmse:0.869608 test-rmse:1.015277 [9] train-rmse:0.689012 test-rmse:0.850792 [10] train-rmse:0.577190 test-rmse:0.750814 [11] train-rmse:0.509202 test-rmse:0.691211 [12] train-rmse:0.469336 test-rmse:0.652569 [13] train-rmse:0.446522 test-rmse:0.637349 [14] train-rmse:0.433832 test-rmse:0.624833 [15] train-rmse:0.418183 test-rmse:0.618716 [16] train-rmse:0.411496 test-rmse:0.615132 [17] train-rmse:0.407561 test-rmse:0.614640 [18] train-rmse:0.404525 test-rmse:0.615013 [19] train-rmse:0.396309 test-rmse:0.610478 [20] train-rmse:0.386871 test-rmse:0.614982 [1] train-rmse:8.688326 test-rmse:8.528812 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.100250 test-rmse:5.946250 [3] train-rmse:4.294040 test-rmse:4.148230 [4] train-rmse:3.036149 test-rmse:2.916992 [5] train-rmse:2.162748 test-rmse:2.085425 [6] train-rmse:1.559979 test-rmse:1.521738 [7] train-rmse:1.145992 test-rmse:1.173055 [8] train-rmse:0.869672 test-rmse:0.963717 [9] train-rmse:0.691519 test-rmse:0.847016 [10] train-rmse:0.576713 test-rmse:0.785416 [11] train-rmse:0.508042 test-rmse:0.760332 [12] train-rmse:0.467233 test-rmse:0.751616 [13] train-rmse:0.442106 test-rmse:0.752072 [14] train-rmse:0.427766 test-rmse:0.754568 [15] train-rmse:0.416442 test-rmse:0.756617 Stopping. Best iteration: [12] train-rmse:0.467233 test-rmse:0.751616
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.757943 test-rmse:8.699812 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.144021 test-rmse:6.098456 [3] train-rmse:4.318181 test-rmse:4.290560 [4] train-rmse:3.046134 test-rmse:3.043893 [5] train-rmse:2.164487 test-rmse:2.197340 [6] train-rmse:1.558953 test-rmse:1.640623 [7] train-rmse:1.149353 test-rmse:1.289704 [8] train-rmse:0.879557 test-rmse:1.081563 [9] train-rmse:0.706479 test-rmse:0.969921 [10] train-rmse:0.593346 test-rmse:0.915709 [11] train-rmse:0.524071 test-rmse:0.888667 [12] train-rmse:0.484919 test-rmse:0.879524 [13] train-rmse:0.465200 test-rmse:0.875791 [14] train-rmse:0.447886 test-rmse:0.875671 [15] train-rmse:0.439200 test-rmse:0.876538 [16] train-rmse:0.429569 test-rmse:0.879385 [17] train-rmse:0.421960 test-rmse:0.883003 Stopping. Best iteration: [14] train-rmse:0.447886 test-rmse:0.875671 [1] train-rmse:8.749935 test-rmse:8.755291 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.139223 test-rmse:6.153017 [3] train-rmse:4.315973 test-rmse:4.341690 [4] train-rmse:3.046217 test-rmse:3.088569 [5] train-rmse:2.166817 test-rmse:2.231806 [6] train-rmse:1.563754 test-rmse:1.654685 [7] train-rmse:1.156216 test-rmse:1.282338 [8] train-rmse:0.890136 test-rmse:1.054025 [9] train-rmse:0.718354 test-rmse:0.917155 [10] train-rmse:0.616464 test-rmse:0.857181 [11] train-rmse:0.554290 test-rmse:0.808472 [12] train-rmse:0.516205 test-rmse:0.800415 [13] train-rmse:0.495257 test-rmse:0.799269 [14] train-rmse:0.482786 test-rmse:0.799148 [15] train-rmse:0.464374 test-rmse:0.801271 [16] train-rmse:0.457960 test-rmse:0.803272 [17] train-rmse:0.447751 test-rmse:0.797663 [18] train-rmse:0.445044 test-rmse:0.799846 [19] train-rmse:0.443391 test-rmse:0.800645 [20] train-rmse:0.441248 test-rmse:0.804031 Stopping. Best iteration: [17] train-rmse:0.447751 test-rmse:0.797663 [1] train-rmse:8.733138 test-rmse:8.868910 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.127738 test-rmse:6.264448 [3] train-rmse:4.308324 test-rmse:4.446187 [4] train-rmse:3.041416 test-rmse:3.180558 [5] train-rmse:2.164030 test-rmse:2.309771 [6] train-rmse:1.562513 test-rmse:1.708147 [7] train-rmse:1.157068 test-rmse:1.302295 [8] train-rmse:0.890427 test-rmse:1.037229 [9] train-rmse:0.716330 test-rmse:0.870427 [10] train-rmse:0.602302 test-rmse:0.766767 [11] train-rmse:0.536771 test-rmse:0.707194 [12] train-rmse:0.494412 test-rmse:0.667216 [13] train-rmse:0.473346 test-rmse:0.651290 [14] train-rmse:0.457713 test-rmse:0.640778 [15] train-rmse:0.451308 test-rmse:0.634766 [16] train-rmse:0.446084 test-rmse:0.631715 [17] train-rmse:0.431155 test-rmse:0.630871 [18] train-rmse:0.427503 test-rmse:0.627885 [19] train-rmse:0.423084 test-rmse:0.627131 [20] train-rmse:0.413872 test-rmse:0.632389 [1] train-rmse:8.759303 test-rmse:8.688820 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.145145 test-rmse:6.088672 [3] train-rmse:4.319214 test-rmse:4.282774 [4] train-rmse:3.047206 test-rmse:3.039128 [5] train-rmse:2.165725 test-rmse:2.196879 [6] train-rmse:1.560664 test-rmse:1.648206 [7] train-rmse:1.151747 test-rmse:1.307000 [8] train-rmse:0.883188 test-rmse:1.107719 [9] train-rmse:0.712312 test-rmse:0.999253 [10] train-rmse:0.603686 test-rmse:0.947985 [11] train-rmse:0.532499 test-rmse:0.926399 [12] train-rmse:0.488823 test-rmse:0.918308 [13] train-rmse:0.460111 test-rmse:0.914912 [14] train-rmse:0.440861 test-rmse:0.917548 [15] train-rmse:0.434783 test-rmse:0.919435 [16] train-rmse:0.431564 test-rmse:0.921430 Stopping. Best iteration: [13] train-rmse:0.460111 test-rmse:0.914912 [1] train-rmse:8.766030 test-rmse:8.648162 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.149419 test-rmse:6.043884 [3] train-rmse:4.321588 test-rmse:4.233859 [4] train-rmse:3.047994 test-rmse:2.985838 [5] train-rmse:2.165054 test-rmse:2.139018 [6] train-rmse:1.558616 test-rmse:1.583365 [7] train-rmse:1.147421 test-rmse:1.234317 [8] train-rmse:0.878242 test-rmse:1.034506 [9] train-rmse:0.704404 test-rmse:0.932090 [10] train-rmse:0.592757 test-rmse:0.887392 [11] train-rmse:0.520377 test-rmse:0.865346 [12] train-rmse:0.480291 test-rmse:0.860938 [13] train-rmse:0.453509 test-rmse:0.861157 [14] train-rmse:0.440430 test-rmse:0.862147 [15] train-rmse:0.431913 test-rmse:0.864404 Stopping. Best iteration: [12] train-rmse:0.480291 test-rmse:0.860938
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.305587 test-rmse:9.400786 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.544202 test-rmse:6.640206 [3] train-rmse:4.620001 test-rmse:4.708942 [4] train-rmse:3.286138 test-rmse:3.379570 [5] train-rmse:2.369486 test-rmse:2.468950 [6] train-rmse:1.745751 test-rmse:1.868049 [7] train-rmse:1.335865 test-rmse:1.476749 [8] train-rmse:1.073154 test-rmse:1.243835 [9] train-rmse:0.913911 test-rmse:1.115281 [10] train-rmse:0.823954 test-rmse:1.039925 [11] train-rmse:0.770249 test-rmse:0.999084 [12] train-rmse:0.736583 test-rmse:0.977358 [13] train-rmse:0.710935 test-rmse:0.972702 [14] train-rmse:0.702447 test-rmse:0.969836 [15] train-rmse:0.696851 test-rmse:0.968553 [16] train-rmse:0.693685 test-rmse:0.965608 [17] train-rmse:0.689690 test-rmse:0.966914 [18] train-rmse:0.678607 test-rmse:0.969539 [19] train-rmse:0.673509 test-rmse:0.967548 Stopping. Best iteration: [16] train-rmse:0.693685 test-rmse:0.965608 [1] train-rmse:9.308211 test-rmse:9.370200 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.545988 test-rmse:6.625093 [3] train-rmse:4.621527 test-rmse:4.718648 [4] train-rmse:3.287398 test-rmse:3.406908 [5] train-rmse:2.368819 test-rmse:2.518749 [6] train-rmse:1.746359 test-rmse:1.931976 [7] train-rmse:1.337223 test-rmse:1.555523 [8] train-rmse:1.077628 test-rmse:1.326685 [9] train-rmse:0.916765 test-rmse:1.196960 [10] train-rmse:0.825072 test-rmse:1.123729 [11] train-rmse:0.771997 test-rmse:1.082401 [12] train-rmse:0.738581 test-rmse:1.061836 [13] train-rmse:0.722903 test-rmse:1.049529 [14] train-rmse:0.706453 test-rmse:1.046084 [15] train-rmse:0.701255 test-rmse:1.042553 [16] train-rmse:0.696841 test-rmse:1.039568 [17] train-rmse:0.685662 test-rmse:1.040522 [18] train-rmse:0.677747 test-rmse:1.038435 [19] train-rmse:0.671002 test-rmse:1.038883 [20] train-rmse:0.669211 test-rmse:1.039282 [1] train-rmse:9.358873 test-rmse:9.001379 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.580398 test-rmse:6.226299 [3] train-rmse:4.643775 test-rmse:4.299111 [4] train-rmse:3.300261 test-rmse:2.973937 [5] train-rmse:2.375797 test-rmse:2.098247 [6] train-rmse:1.749968 test-rmse:1.566600 [7] train-rmse:1.335963 test-rmse:1.267015 [8] train-rmse:1.073817 test-rmse:1.139219 [9] train-rmse:0.913497 test-rmse:1.097007 [10] train-rmse:0.820858 test-rmse:1.099757 [11] train-rmse:0.764215 test-rmse:1.118004 [12] train-rmse:0.734321 test-rmse:1.139633 Stopping. Best iteration: [9] train-rmse:0.913497 test-rmse:1.097007 [1] train-rmse:9.317182 test-rmse:9.329130 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.551151 test-rmse:6.578157 [3] train-rmse:4.623536 test-rmse:4.654675 [4] train-rmse:3.286454 test-rmse:3.331844 [5] train-rmse:2.365457 test-rmse:2.433483 [6] train-rmse:1.743695 test-rmse:1.843876 [7] train-rmse:1.334003 test-rmse:1.474071 [8] train-rmse:1.071737 test-rmse:1.252720 [9] train-rmse:0.913643 test-rmse:1.119606 [10] train-rmse:0.822335 test-rmse:1.052755 [11] train-rmse:0.770054 test-rmse:1.015252 [12] train-rmse:0.740329 test-rmse:1.002755 [13] train-rmse:0.721946 test-rmse:0.996629 [14] train-rmse:0.713661 test-rmse:0.993183 [15] train-rmse:0.709133 test-rmse:0.993391 [16] train-rmse:0.698999 test-rmse:0.995279 [17] train-rmse:0.696526 test-rmse:0.995984 Stopping. Best iteration: [14] train-rmse:0.713661 test-rmse:0.993183 [1] train-rmse:9.315177 test-rmse:9.388052 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.552079 test-rmse:6.720072 [3] train-rmse:4.627710 test-rmse:4.860412 [4] train-rmse:3.294992 test-rmse:3.560642 [5] train-rmse:2.379532 test-rmse:2.625129 [6] train-rmse:1.760649 test-rmse:1.999409 [7] train-rmse:1.354903 test-rmse:1.568141 [8] train-rmse:1.094174 test-rmse:1.282121 [9] train-rmse:0.940164 test-rmse:1.101472 [10] train-rmse:0.845439 test-rmse:0.979132 [11] train-rmse:0.793198 test-rmse:0.898458 [12] train-rmse:0.763638 test-rmse:0.854345 [13] train-rmse:0.745383 test-rmse:0.827975 [14] train-rmse:0.726969 test-rmse:0.808252 [15] train-rmse:0.721611 test-rmse:0.792426 [16] train-rmse:0.714775 test-rmse:0.788604 [17] train-rmse:0.706202 test-rmse:0.782618 [18] train-rmse:0.704975 test-rmse:0.778979 [19] train-rmse:0.702987 test-rmse:0.775779 [20] train-rmse:0.701582 test-rmse:0.773678
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.677403 test-rmse:8.679509 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.096570 test-rmse:6.026643 [3] train-rmse:4.294943 test-rmse:4.174021 [4] train-rmse:3.041826 test-rmse:2.885673 [5] train-rmse:2.176603 test-rmse:1.998467 [6] train-rmse:1.586110 test-rmse:1.398579 [7] train-rmse:1.193103 test-rmse:1.009659 [8] train-rmse:0.940363 test-rmse:0.775433 [9] train-rmse:0.787427 test-rmse:0.657743 [10] train-rmse:0.698511 test-rmse:0.609206 [11] train-rmse:0.650057 test-rmse:0.597903 [12] train-rmse:0.624550 test-rmse:0.603597 [13] train-rmse:0.611486 test-rmse:0.612505 [14] train-rmse:0.604746 test-rmse:0.622147 Stopping. Best iteration: [11] train-rmse:0.650057 test-rmse:0.597903 [1] train-rmse:8.601503 test-rmse:9.119301 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.041714 test-rmse:6.532770 [3] train-rmse:4.254190 test-rmse:4.724894 [4] train-rmse:3.009844 test-rmse:3.474501 [5] train-rmse:2.148830 test-rmse:2.609264 [6] train-rmse:1.559523 test-rmse:2.019741 [7] train-rmse:1.165544 test-rmse:1.614694 [8] train-rmse:0.910201 test-rmse:1.342406 [9] train-rmse:0.752582 test-rmse:1.163025 [10] train-rmse:0.660743 test-rmse:1.043828 [11] train-rmse:0.610203 test-rmse:0.966072 [12] train-rmse:0.583607 test-rmse:0.915356 [13] train-rmse:0.569847 test-rmse:0.882138 [14] train-rmse:0.562817 test-rmse:0.859420 [15] train-rmse:0.559252 test-rmse:0.843701 [16] train-rmse:0.557436 test-rmse:0.833348 [17] train-rmse:0.556505 test-rmse:0.826008 [18] train-rmse:0.555972 test-rmse:0.821252 [19] train-rmse:0.555674 test-rmse:0.817527 [20] train-rmse:0.555524 test-rmse:0.815022 [1] train-rmse:8.691648 test-rmse:8.580938 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.106111 test-rmse:5.994452 [3] train-rmse:4.301468 test-rmse:4.203064 [4] train-rmse:3.046359 test-rmse:2.956926 [5] train-rmse:2.179783 test-rmse:2.101988 [6] train-rmse:1.589640 test-rmse:1.526298 [7] train-rmse:1.197149 test-rmse:1.147766 [8] train-rmse:0.945859 test-rmse:0.917341 [9] train-rmse:0.792951 test-rmse:0.786292 [10] train-rmse:0.705774 test-rmse:0.717596 [11] train-rmse:0.657950 test-rmse:0.685041 [12] train-rmse:0.632827 test-rmse:0.671191 [13] train-rmse:0.620003 test-rmse:0.665538 [14] train-rmse:0.613451 test-rmse:0.664450 [15] train-rmse:0.610130 test-rmse:0.665048 [16] train-rmse:0.608447 test-rmse:0.666183 [17] train-rmse:0.607537 test-rmse:0.667235 Stopping. Best iteration: [14] train-rmse:0.613451 test-rmse:0.664450 [1] train-rmse:8.746120 test-rmse:8.248809 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.144679 test-rmse:5.657545 [3] train-rmse:4.329529 test-rmse:3.889060 [4] train-rmse:3.067348 test-rmse:2.657838 [5] train-rmse:2.196090 test-rmse:1.800081 [6] train-rmse:1.602963 test-rmse:1.217842 [7] train-rmse:1.207307 test-rmse:0.839898 [8] train-rmse:0.953908 test-rmse:0.618283 [9] train-rmse:0.800706 test-rmse:0.515078 [10] train-rmse:0.711933 test-rmse:0.486762 [11] train-rmse:0.663315 test-rmse:0.493514 [12] train-rmse:0.637898 test-rmse:0.511116 [13] train-rmse:0.624559 test-rmse:0.528963 Stopping. Best iteration: [10] train-rmse:0.711933 test-rmse:0.486762 [1] train-rmse:8.659225 test-rmse:8.781620 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.081006 test-rmse:6.205407 [3] train-rmse:4.281151 test-rmse:4.421570 [4] train-rmse:3.028425 test-rmse:3.188095 [5] train-rmse:2.162068 test-rmse:2.344672 [6] train-rmse:1.569390 test-rmse:1.790758 [7] train-rmse:1.172761 test-rmse:1.437853 [8] train-rmse:0.916513 test-rmse:1.221191 [9] train-rmse:0.759607 test-rmse:1.086349 [10] train-rmse:0.668299 test-rmse:1.017921 [11] train-rmse:0.618623 test-rmse:0.971853 [12] train-rmse:0.592167 test-rmse:0.951778 [13] train-rmse:0.578498 test-rmse:0.941415 [14] train-rmse:0.571545 test-rmse:0.933321 [15] train-rmse:0.567981 test-rmse:0.928247 [16] train-rmse:0.566149 test-rmse:0.925782 [17] train-rmse:0.565190 test-rmse:0.923907 [18] train-rmse:0.564677 test-rmse:0.923147 [19] train-rmse:0.564386 test-rmse:0.922381 [20] train-rmse:0.564229 test-rmse:0.921943
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.207486 test-rmse:8.991811 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.467295 test-rmse:6.253538 [3] train-rmse:4.556628 test-rmse:4.371399 [4] train-rmse:3.229757 test-rmse:3.071446 [5] train-rmse:2.314905 test-rmse:2.184901 [6] train-rmse:1.693905 test-rmse:1.597192 [7] train-rmse:1.282872 test-rmse:1.229009 [8] train-rmse:1.022167 test-rmse:1.015512 [9] train-rmse:0.865269 test-rmse:0.906922 [10] train-rmse:0.776477 test-rmse:0.858013 [11] train-rmse:0.728524 test-rmse:0.839494 [12] train-rmse:0.703660 test-rmse:0.835429 [13] train-rmse:0.691042 test-rmse:0.836826 [14] train-rmse:0.684704 test-rmse:0.840090 [15] train-rmse:0.681529 test-rmse:0.843703 Stopping. Best iteration: [12] train-rmse:0.703660 test-rmse:0.835429 [1] train-rmse:9.159916 test-rmse:9.277818 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.431822 test-rmse:6.558334 [3] train-rmse:4.529216 test-rmse:4.663330 [4] train-rmse:3.207231 test-rmse:3.356185 [5] train-rmse:2.295244 test-rmse:2.464667 [6] train-rmse:1.674278 test-rmse:1.863263 [7] train-rmse:1.262332 test-rmse:1.478598 [8] train-rmse:0.999096 test-rmse:1.243468 [9] train-rmse:0.839037 test-rmse:1.108800 [10] train-rmse:0.747471 test-rmse:1.034727 [11] train-rmse:0.697767 test-rmse:0.995072 [12] train-rmse:0.671832 test-rmse:0.973588 [13] train-rmse:0.658527 test-rmse:0.962499 [14] train-rmse:0.651821 test-rmse:0.956340 [15] train-rmse:0.648379 test-rmse:0.953651 [16] train-rmse:0.646608 test-rmse:0.952809 [17] train-rmse:0.645703 test-rmse:0.952190 [18] train-rmse:0.645232 test-rmse:0.952016 [19] train-rmse:0.644984 test-rmse:0.952009 [20] train-rmse:0.644850 test-rmse:0.952086 [1] train-rmse:9.173143 test-rmse:9.230320 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.442726 test-rmse:6.520811 [3] train-rmse:4.538280 test-rmse:4.632806 [4] train-rmse:3.214861 test-rmse:3.333598 [5] train-rmse:2.302082 test-rmse:2.443955 [6] train-rmse:1.681617 test-rmse:1.849716 [7] train-rmse:1.270516 test-rmse:1.462872 [8] train-rmse:1.008914 test-rmse:1.222106 [9] train-rmse:0.851015 test-rmse:1.080121 [10] train-rmse:0.760878 test-rmse:0.999896 [11] train-rmse:0.712260 test-rmse:0.954777 [12] train-rmse:0.687002 test-rmse:0.928772 [13] train-rmse:0.674171 test-rmse:0.914398 [14] train-rmse:0.667742 test-rmse:0.905998 [15] train-rmse:0.664522 test-rmse:0.900924 [16] train-rmse:0.662916 test-rmse:0.897831 [17] train-rmse:0.662112 test-rmse:0.895868 [18] train-rmse:0.661708 test-rmse:0.894598 [19] train-rmse:0.661498 test-rmse:0.893920 [20] train-rmse:0.661391 test-rmse:0.893368 [1] train-rmse:9.195619 test-rmse:9.063790 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.459298 test-rmse:6.335691 [3] train-rmse:4.551364 test-rmse:4.415663 [4] train-rmse:3.225669 test-rmse:3.088830 [5] train-rmse:2.311709 test-rmse:2.190937 [6] train-rmse:1.690759 test-rmse:1.599862 [7] train-rmse:1.279845 test-rmse:1.233754 [8] train-rmse:1.018356 test-rmse:1.030390 [9] train-rmse:0.860887 test-rmse:0.928608 [10] train-rmse:0.771390 test-rmse:0.887863 [11] train-rmse:0.723106 test-rmse:0.876597 [12] train-rmse:0.697626 test-rmse:0.877650 [13] train-rmse:0.684568 test-rmse:0.883915 [14] train-rmse:0.677917 test-rmse:0.889617 Stopping. Best iteration: [11] train-rmse:0.723106 test-rmse:0.876597 [1] train-rmse:9.173735 test-rmse:9.210232 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.445267 test-rmse:6.501317 [3] train-rmse:4.543175 test-rmse:4.600963 [4] train-rmse:3.222389 test-rmse:3.289716 [5] train-rmse:2.312612 test-rmse:2.386741 [6] train-rmse:1.695789 test-rmse:1.773374 [7] train-rmse:1.289167 test-rmse:1.370154 [8] train-rmse:1.032129 test-rmse:1.108526 [9] train-rmse:0.878075 test-rmse:0.947520 [10] train-rmse:0.790959 test-rmse:0.853173 [11] train-rmse:0.744227 test-rmse:0.799232 [12] train-rmse:0.719992 test-rmse:0.767839 [13] train-rmse:0.707364 test-rmse:0.749887 [14] train-rmse:0.700946 test-rmse:0.739529 [15] train-rmse:0.697695 test-rmse:0.733092 [16] train-rmse:0.696003 test-rmse:0.729489 [17] train-rmse:0.695128 test-rmse:0.727470 [18] train-rmse:0.694669 test-rmse:0.725992 [19] train-rmse:0.694422 test-rmse:0.725150 [20] train-rmse:0.694289 test-rmse:0.724602
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.508665 test-rmse:9.731531 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.706135 test-rmse:6.951339 [3] train-rmse:4.759617 test-rmse:5.013197 [4] train-rmse:3.413927 test-rmse:3.701121 [5] train-rmse:2.495826 test-rmse:2.789298 [6] train-rmse:1.880611 test-rmse:2.189666 [7] train-rmse:1.481085 test-rmse:1.805017 [8] train-rmse:1.226560 test-rmse:1.568385 [9] train-rmse:1.077420 test-rmse:1.429082 [10] train-rmse:0.984856 test-rmse:1.342703 [11] train-rmse:0.935189 test-rmse:1.291978 [12] train-rmse:0.902633 test-rmse:1.261104 [13] train-rmse:0.886718 test-rmse:1.238138 [14] train-rmse:0.872524 test-rmse:1.236695 [15] train-rmse:0.864078 test-rmse:1.229511 [16] train-rmse:0.851614 test-rmse:1.231070 [17] train-rmse:0.844923 test-rmse:1.225900 [18] train-rmse:0.841224 test-rmse:1.225529 [19] train-rmse:0.837563 test-rmse:1.232424 [20] train-rmse:0.835681 test-rmse:1.231445 [1] train-rmse:9.542632 test-rmse:9.501602 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.728758 test-rmse:6.689415 [3] train-rmse:4.774064 test-rmse:4.734704 [4] train-rmse:3.425067 test-rmse:3.396367 [5] train-rmse:2.502941 test-rmse:2.499803 [6] train-rmse:1.887453 test-rmse:1.916760 [7] train-rmse:1.486156 test-rmse:1.547914 [8] train-rmse:1.234054 test-rmse:1.344133 [9] train-rmse:1.081624 test-rmse:1.250620 [10] train-rmse:0.994028 test-rmse:1.190282 [11] train-rmse:0.938222 test-rmse:1.178410 [12] train-rmse:0.907100 test-rmse:1.167284 [13] train-rmse:0.880580 test-rmse:1.174441 [14] train-rmse:0.869859 test-rmse:1.172513 [15] train-rmse:0.846606 test-rmse:1.176107 Stopping. Best iteration: [12] train-rmse:0.907100 test-rmse:1.167284 [1] train-rmse:9.607680 test-rmse:9.186364 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.772362 test-rmse:6.396841 [3] train-rmse:4.800287 test-rmse:4.505610 [4] train-rmse:3.438562 test-rmse:3.255769 [5] train-rmse:2.508472 test-rmse:2.474618 [6] train-rmse:1.883570 test-rmse:2.052852 [7] train-rmse:1.474396 test-rmse:1.827297 [8] train-rmse:1.215900 test-rmse:1.728341 [9] train-rmse:1.064737 test-rmse:1.704244 [10] train-rmse:0.968524 test-rmse:1.707443 [11] train-rmse:0.920825 test-rmse:1.720676 [12] train-rmse:0.885365 test-rmse:1.735631 Stopping. Best iteration: [9] train-rmse:1.064737 test-rmse:1.704244 [1] train-rmse:9.546103 test-rmse:9.455536 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.730136 test-rmse:6.654431 [3] train-rmse:4.773525 test-rmse:4.712755 [4] train-rmse:3.420798 test-rmse:3.404648 [5] train-rmse:2.498316 test-rmse:2.530076 [6] train-rmse:1.882774 test-rmse:1.972420 [7] train-rmse:1.478224 test-rmse:1.636997 [8] train-rmse:1.228847 test-rmse:1.455971 [9] train-rmse:1.073135 test-rmse:1.377705 [10] train-rmse:0.978145 test-rmse:1.340181 [11] train-rmse:0.922110 test-rmse:1.333576 [12] train-rmse:0.891518 test-rmse:1.329818 [13] train-rmse:0.870973 test-rmse:1.330972 [14] train-rmse:0.856070 test-rmse:1.343441 [15] train-rmse:0.843758 test-rmse:1.344300 Stopping. Best iteration: [12] train-rmse:0.891518 test-rmse:1.329818 [1] train-rmse:9.470266 test-rmse:9.998213 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.676673 test-rmse:7.192227 [3] train-rmse:4.735589 test-rmse:5.251865 [4] train-rmse:3.395657 test-rmse:3.908945 [5] train-rmse:2.480836 test-rmse:2.992138 [6] train-rmse:1.867794 test-rmse:2.378305 [7] train-rmse:1.469569 test-rmse:1.975446 [8] train-rmse:1.219034 test-rmse:1.729437 [9] train-rmse:1.062272 test-rmse:1.581387 [10] train-rmse:0.973005 test-rmse:1.496163 [11] train-rmse:0.919845 test-rmse:1.442745 [12] train-rmse:0.891768 test-rmse:1.408264 [13] train-rmse:0.876095 test-rmse:1.391779 [14] train-rmse:0.859240 test-rmse:1.378712 [15] train-rmse:0.850033 test-rmse:1.376405 [16] train-rmse:0.840988 test-rmse:1.373204 [17] train-rmse:0.838035 test-rmse:1.372692 [18] train-rmse:0.835218 test-rmse:1.371418 [19] train-rmse:0.825326 test-rmse:1.373756 [20] train-rmse:0.807915 test-rmse:1.381289
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.074142 test-rmse:9.167627 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.370614 test-rmse:6.469861 [3] train-rmse:4.483422 test-rmse:4.594169 [4] train-rmse:3.170002 test-rmse:3.299574 [5] train-rmse:2.260724 test-rmse:2.415683 [6] train-rmse:1.634990 test-rmse:1.811309 [7] train-rmse:1.212637 test-rmse:1.418665 [8] train-rmse:0.933383 test-rmse:1.174073 [9] train-rmse:0.756651 test-rmse:1.026131 [10] train-rmse:0.650565 test-rmse:0.941928 [11] train-rmse:0.587117 test-rmse:0.897072 [12] train-rmse:0.548309 test-rmse:0.871289 [13] train-rmse:0.523473 test-rmse:0.857596 [14] train-rmse:0.511957 test-rmse:0.851018 [15] train-rmse:0.506151 test-rmse:0.846908 [16] train-rmse:0.497885 test-rmse:0.843274 [17] train-rmse:0.496037 test-rmse:0.843502 [18] train-rmse:0.490962 test-rmse:0.844016 [19] train-rmse:0.490237 test-rmse:0.844633 Stopping. Best iteration: [16] train-rmse:0.497885 test-rmse:0.843274 [1] train-rmse:9.120785 test-rmse:8.859761 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.402043 test-rmse:6.154218 [3] train-rmse:4.503564 test-rmse:4.267583 [4] train-rmse:3.180747 test-rmse:2.975774 [5] train-rmse:2.262919 test-rmse:2.106082 [6] train-rmse:1.632761 test-rmse:1.546576 [7] train-rmse:1.206959 test-rmse:1.215203 [8] train-rmse:0.924702 test-rmse:1.042722 [9] train-rmse:0.744818 test-rmse:0.966129 [10] train-rmse:0.635528 test-rmse:0.947490 [11] train-rmse:0.567988 test-rmse:0.948465 [12] train-rmse:0.527371 test-rmse:0.957052 [13] train-rmse:0.505433 test-rmse:0.966440 Stopping. Best iteration: [10] train-rmse:0.635528 test-rmse:0.947490 [1] train-rmse:9.122522 test-rmse:8.899934 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.405373 test-rmse:6.206759 [3] train-rmse:4.509176 test-rmse:4.328043 [4] train-rmse:3.189461 test-rmse:3.025670 [5] train-rmse:2.276989 test-rmse:2.136511 [6] train-rmse:1.652850 test-rmse:1.548324 [7] train-rmse:1.228631 test-rmse:1.165855 [8] train-rmse:0.948333 test-rmse:0.946542 [9] train-rmse:0.772713 test-rmse:0.827683 [10] train-rmse:0.662839 test-rmse:0.767501 [11] train-rmse:0.599415 test-rmse:0.746821 [12] train-rmse:0.566466 test-rmse:0.739341 [13] train-rmse:0.541987 test-rmse:0.735530 [14] train-rmse:0.530753 test-rmse:0.738711 [15] train-rmse:0.520279 test-rmse:0.741017 [16] train-rmse:0.517642 test-rmse:0.742984 Stopping. Best iteration: [13] train-rmse:0.541987 test-rmse:0.735530 [1] train-rmse:9.060252 test-rmse:9.254415 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.363393 test-rmse:6.529369 [3] train-rmse:4.482027 test-rmse:4.623081 [4] train-rmse:3.172258 test-rmse:3.291727 [5] train-rmse:2.266517 test-rmse:2.377180 [6] train-rmse:1.646501 test-rmse:1.739860 [7] train-rmse:1.230823 test-rmse:1.298050 [8] train-rmse:0.961788 test-rmse:0.997888 [9] train-rmse:0.791928 test-rmse:0.794635 [10] train-rmse:0.688016 test-rmse:0.661070 [11] train-rmse:0.630221 test-rmse:0.574394 [12] train-rmse:0.595975 test-rmse:0.519969 [13] train-rmse:0.573497 test-rmse:0.482897 [14] train-rmse:0.560343 test-rmse:0.472329 [15] train-rmse:0.551086 test-rmse:0.457750 [16] train-rmse:0.539154 test-rmse:0.454339 [17] train-rmse:0.537553 test-rmse:0.448755 [18] train-rmse:0.531609 test-rmse:0.446835 [19] train-rmse:0.527663 test-rmse:0.443805 [20] train-rmse:0.524602 test-rmse:0.445440 [1] train-rmse:9.075330 test-rmse:9.193081 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.371068 test-rmse:6.500402 [3] train-rmse:4.483127 test-rmse:4.614854 [4] train-rmse:3.168680 test-rmse:3.305673 [5] train-rmse:2.259473 test-rmse:2.406363 [6] train-rmse:1.632942 test-rmse:1.792150 [7] train-rmse:1.208939 test-rmse:1.381213 [8] train-rmse:0.930638 test-rmse:1.116618 [9] train-rmse:0.754089 test-rmse:0.947582 [10] train-rmse:0.647129 test-rmse:0.846393 [11] train-rmse:0.582002 test-rmse:0.783919 [12] train-rmse:0.544076 test-rmse:0.751761 [13] train-rmse:0.522648 test-rmse:0.732090 [14] train-rmse:0.510692 test-rmse:0.720503 [15] train-rmse:0.503713 test-rmse:0.713396 [16] train-rmse:0.500311 test-rmse:0.709784 [17] train-rmse:0.495757 test-rmse:0.707839 [18] train-rmse:0.494483 test-rmse:0.708158 [19] train-rmse:0.488482 test-rmse:0.706636 [20] train-rmse:0.485721 test-rmse:0.706565
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.115062 test-rmse:9.428317 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.403851 test-rmse:6.692894 [3] train-rmse:4.513468 test-rmse:4.788183 [4] train-rmse:3.201513 test-rmse:3.467120 [5] train-rmse:2.297875 test-rmse:2.545228 [6] train-rmse:1.683513 test-rmse:1.928571 [7] train-rmse:1.271901 test-rmse:1.515190 [8] train-rmse:1.006224 test-rmse:1.255235 [9] train-rmse:0.842992 test-rmse:1.094131 [10] train-rmse:0.749364 test-rmse:0.998002 [11] train-rmse:0.695483 test-rmse:0.944403 [12] train-rmse:0.665159 test-rmse:0.914458 [13] train-rmse:0.644734 test-rmse:0.898429 [14] train-rmse:0.636863 test-rmse:0.886571 [15] train-rmse:0.626368 test-rmse:0.880346 [16] train-rmse:0.621907 test-rmse:0.876254 [17] train-rmse:0.619923 test-rmse:0.873246 [18] train-rmse:0.618474 test-rmse:0.871559 [19] train-rmse:0.617409 test-rmse:0.870364 [20] train-rmse:0.612286 test-rmse:0.870502 [1] train-rmse:9.171700 test-rmse:9.079977 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.442400 test-rmse:6.351373 [3] train-rmse:4.539413 test-rmse:4.467994 [4] train-rmse:3.218076 test-rmse:3.156707 [5] train-rmse:2.306843 test-rmse:2.267150 [6] train-rmse:1.685391 test-rmse:1.671830 [7] train-rmse:1.272426 test-rmse:1.294761 [8] train-rmse:1.004983 test-rmse:1.069968 [9] train-rmse:0.841561 test-rmse:0.948027 [10] train-rmse:0.742954 test-rmse:0.886700 [11] train-rmse:0.686553 test-rmse:0.860296 [12] train-rmse:0.653543 test-rmse:0.850947 [13] train-rmse:0.635837 test-rmse:0.850136 [14] train-rmse:0.625231 test-rmse:0.849060 [15] train-rmse:0.617974 test-rmse:0.850980 [16] train-rmse:0.614422 test-rmse:0.852549 [17] train-rmse:0.609557 test-rmse:0.854139 Stopping. Best iteration: [14] train-rmse:0.625231 test-rmse:0.849060 [1] train-rmse:9.232126 test-rmse:8.670464 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.483522 test-rmse:5.943523 [3] train-rmse:4.566509 test-rmse:4.048058 [4] train-rmse:3.234110 test-rmse:2.765894 [5] train-rmse:2.315118 test-rmse:1.911611 [6] train-rmse:1.690193 test-rmse:1.377615 [7] train-rmse:1.270208 test-rmse:1.095447 [8] train-rmse:0.996386 test-rmse:0.978843 [9] train-rmse:0.827807 test-rmse:0.951203 [10] train-rmse:0.732187 test-rmse:0.958040 [11] train-rmse:0.675850 test-rmse:0.981917 [12] train-rmse:0.643549 test-rmse:1.004649 Stopping. Best iteration: [9] train-rmse:0.827807 test-rmse:0.951203 [1] train-rmse:9.178247 test-rmse:9.032164 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.447485 test-rmse:6.323424 [3] train-rmse:4.543427 test-rmse:4.445379 [4] train-rmse:3.221248 test-rmse:3.152184 [5] train-rmse:2.309496 test-rmse:2.267009 [6] train-rmse:1.690113 test-rmse:1.686766 [7] train-rmse:1.276404 test-rmse:1.320876 [8] train-rmse:1.010846 test-rmse:1.103512 [9] train-rmse:0.847056 test-rmse:0.992795 [10] train-rmse:0.752428 test-rmse:0.938707 [11] train-rmse:0.699411 test-rmse:0.914055 [12] train-rmse:0.671300 test-rmse:0.905483 [13] train-rmse:0.654959 test-rmse:0.905450 [14] train-rmse:0.645009 test-rmse:0.905922 [15] train-rmse:0.640056 test-rmse:0.908005 [16] train-rmse:0.635554 test-rmse:0.910771 Stopping. Best iteration: [13] train-rmse:0.654959 test-rmse:0.905450 [1] train-rmse:9.108737 test-rmse:9.466683 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.398773 test-rmse:6.756265 [3] train-rmse:4.509658 test-rmse:4.868750 [4] train-rmse:3.198551 test-rmse:3.558728 [5] train-rmse:2.296444 test-rmse:2.656281 [6] train-rmse:1.684470 test-rmse:2.028710 [7] train-rmse:1.276923 test-rmse:1.611759 [8] train-rmse:1.013226 test-rmse:1.336894 [9] train-rmse:0.854739 test-rmse:1.158877 [10] train-rmse:0.765015 test-rmse:1.048733 [11] train-rmse:0.707957 test-rmse:0.980549 [12] train-rmse:0.678374 test-rmse:0.935328 [13] train-rmse:0.660511 test-rmse:0.905287 [14] train-rmse:0.651344 test-rmse:0.887085 [15] train-rmse:0.644129 test-rmse:0.875462 [16] train-rmse:0.637421 test-rmse:0.870323 [17] train-rmse:0.632798 test-rmse:0.866080 [18] train-rmse:0.626527 test-rmse:0.865528 [19] train-rmse:0.621919 test-rmse:0.863040 [20] train-rmse:0.620878 test-rmse:0.861270
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.685628 test-rmse:9.047315 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.094935 test-rmse:6.405883 [3] train-rmse:4.285530 test-rmse:4.563002 [4] train-rmse:3.024789 test-rmse:3.281277 [5] train-rmse:2.148874 test-rmse:2.396989 [6] train-rmse:1.544719 test-rmse:1.786662 [7] train-rmse:1.131334 test-rmse:1.371120 [8] train-rmse:0.856866 test-rmse:1.097007 [9] train-rmse:0.679897 test-rmse:0.912159 [10] train-rmse:0.569484 test-rmse:0.805673 [11] train-rmse:0.505645 test-rmse:0.740386 [12] train-rmse:0.469492 test-rmse:0.691584 [13] train-rmse:0.449149 test-rmse:0.667494 [14] train-rmse:0.438557 test-rmse:0.654586 [15] train-rmse:0.427167 test-rmse:0.644000 [16] train-rmse:0.422872 test-rmse:0.637604 [17] train-rmse:0.418027 test-rmse:0.631062 [18] train-rmse:0.416465 test-rmse:0.628231 [19] train-rmse:0.411684 test-rmse:0.628689 [20] train-rmse:0.404220 test-rmse:0.626996 [1] train-rmse:8.730112 test-rmse:8.813999 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.126793 test-rmse:6.198964 [3] train-rmse:4.308855 test-rmse:4.372945 [4] train-rmse:3.042079 test-rmse:3.097418 [5] train-rmse:2.163265 test-rmse:2.223511 [6] train-rmse:1.556940 test-rmse:1.618692 [7] train-rmse:1.144739 test-rmse:1.204961 [8] train-rmse:0.873035 test-rmse:0.931280 [9] train-rmse:0.699891 test-rmse:0.756166 [10] train-rmse:0.593695 test-rmse:0.649521 [11] train-rmse:0.529311 test-rmse:0.581360 [12] train-rmse:0.492577 test-rmse:0.543872 [13] train-rmse:0.471978 test-rmse:0.519191 [14] train-rmse:0.460858 test-rmse:0.507815 [15] train-rmse:0.455286 test-rmse:0.500070 [16] train-rmse:0.445133 test-rmse:0.490174 [17] train-rmse:0.441320 test-rmse:0.486804 [18] train-rmse:0.437308 test-rmse:0.486411 [19] train-rmse:0.436116 test-rmse:0.485080 [20] train-rmse:0.430012 test-rmse:0.480978 [1] train-rmse:8.826597 test-rmse:8.362290 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.194737 test-rmse:5.833877 [3] train-rmse:4.357005 test-rmse:4.067338 [4] train-rmse:3.075997 test-rmse:2.837636 [5] train-rmse:2.186144 test-rmse:1.982577 [6] train-rmse:1.572579 test-rmse:1.392046 [7] train-rmse:1.156088 test-rmse:0.995938 [8] train-rmse:0.879314 test-rmse:0.739547 [9] train-rmse:0.700113 test-rmse:0.584789 [10] train-rmse:0.589820 test-rmse:0.505214 [11] train-rmse:0.522201 test-rmse:0.468737 [12] train-rmse:0.487191 test-rmse:0.455575 [13] train-rmse:0.467835 test-rmse:0.453337 [14] train-rmse:0.456352 test-rmse:0.455467 [15] train-rmse:0.445113 test-rmse:0.458624 [16] train-rmse:0.441921 test-rmse:0.461563 Stopping. Best iteration: [13] train-rmse:0.467835 test-rmse:0.453337 [1] train-rmse:8.756870 test-rmse:8.711932 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.144706 test-rmse:6.143349 [3] train-rmse:4.320246 test-rmse:4.353505 [4] train-rmse:3.047886 test-rmse:3.109409 [5] train-rmse:2.163589 test-rmse:2.253155 [6] train-rmse:1.553517 test-rmse:1.673668 [7] train-rmse:1.137553 test-rmse:1.288105 [8] train-rmse:0.862030 test-rmse:1.032470 [9] train-rmse:0.684629 test-rmse:0.878086 [10] train-rmse:0.577073 test-rmse:0.788937 [11] train-rmse:0.514489 test-rmse:0.740400 [12] train-rmse:0.477024 test-rmse:0.716906 [13] train-rmse:0.456477 test-rmse:0.704955 [14] train-rmse:0.442730 test-rmse:0.696741 [15] train-rmse:0.436832 test-rmse:0.694109 [16] train-rmse:0.429696 test-rmse:0.695300 [17] train-rmse:0.421297 test-rmse:0.692949 [18] train-rmse:0.414857 test-rmse:0.695844 [19] train-rmse:0.407839 test-rmse:0.695982 [20] train-rmse:0.404265 test-rmse:0.699231 Stopping. Best iteration: [17] train-rmse:0.421297 test-rmse:0.692949 [1] train-rmse:8.710165 test-rmse:8.835963 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.110902 test-rmse:6.174177 [3] train-rmse:4.294974 test-rmse:4.321983 [4] train-rmse:3.028327 test-rmse:3.028543 [5] train-rmse:2.148525 test-rmse:2.152007 [6] train-rmse:1.538496 test-rmse:1.577601 [7] train-rmse:1.122514 test-rmse:1.219635 [8] train-rmse:0.844770 test-rmse:1.001155 [9] train-rmse:0.664734 test-rmse:0.885369 [10] train-rmse:0.554701 test-rmse:0.822729 [11] train-rmse:0.489987 test-rmse:0.799061 [12] train-rmse:0.452526 test-rmse:0.789851 [13] train-rmse:0.431820 test-rmse:0.786757 [14] train-rmse:0.420794 test-rmse:0.785737 [15] train-rmse:0.411095 test-rmse:0.789235 [16] train-rmse:0.403643 test-rmse:0.789692 [17] train-rmse:0.401635 test-rmse:0.790455 Stopping. Best iteration: [14] train-rmse:0.420794 test-rmse:0.785737
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.420343 test-rmse:8.392657 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.907432 test-rmse:5.881966 [3] train-rmse:4.152614 test-rmse:4.127653 [4] train-rmse:2.929441 test-rmse:2.938648 [5] train-rmse:2.080979 test-rmse:2.116247 [6] train-rmse:1.498033 test-rmse:1.560004 [7] train-rmse:1.104480 test-rmse:1.183995 [8] train-rmse:0.846531 test-rmse:0.939114 [9] train-rmse:0.685228 test-rmse:0.786302 [10] train-rmse:0.589550 test-rmse:0.701499 [11] train-rmse:0.535307 test-rmse:0.652282 [12] train-rmse:0.506121 test-rmse:0.624383 [13] train-rmse:0.490935 test-rmse:0.610474 [14] train-rmse:0.483034 test-rmse:0.602877 [15] train-rmse:0.478962 test-rmse:0.598948 [16] train-rmse:0.476880 test-rmse:0.596276 [17] train-rmse:0.475876 test-rmse:0.594833 [18] train-rmse:0.475351 test-rmse:0.593826 [19] train-rmse:0.474964 test-rmse:0.593718 [20] train-rmse:0.474751 test-rmse:0.593227 [1] train-rmse:8.416825 test-rmse:8.408947 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.905314 test-rmse:5.899548 [3] train-rmse:4.151587 test-rmse:4.144187 [4] train-rmse:2.930137 test-rmse:2.943341 [5] train-rmse:2.083696 test-rmse:2.108681 [6] train-rmse:1.502988 test-rmse:1.525792 [7] train-rmse:1.111597 test-rmse:1.144116 [8] train-rmse:0.855899 test-rmse:0.889734 [9] train-rmse:0.696856 test-rmse:0.728713 [10] train-rmse:0.602667 test-rmse:0.629465 [11] train-rmse:0.549878 test-rmse:0.570826 [12] train-rmse:0.521800 test-rmse:0.536944 [13] train-rmse:0.507387 test-rmse:0.517777 [14] train-rmse:0.499907 test-rmse:0.506715 [15] train-rmse:0.495903 test-rmse:0.500092 [16] train-rmse:0.493826 test-rmse:0.495851 [17] train-rmse:0.492735 test-rmse:0.493266 [18] train-rmse:0.492151 test-rmse:0.491553 [19] train-rmse:0.491830 test-rmse:0.490888 [20] train-rmse:0.491649 test-rmse:0.490074 [1] train-rmse:8.392653 test-rmse:8.557063 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.886595 test-rmse:6.056089 [3] train-rmse:4.135446 test-rmse:4.313589 [4] train-rmse:2.914392 test-rmse:3.104506 [5] train-rmse:2.066498 test-rmse:2.269591 [6] train-rmse:1.482621 test-rmse:1.701370 [7] train-rmse:1.086720 test-rmse:1.316322 [8] train-rmse:0.824625 test-rmse:1.069375 [9] train-rmse:0.658588 test-rmse:0.912869 [10] train-rmse:0.558763 test-rmse:0.818092 [11] train-rmse:0.502347 test-rmse:0.761000 [12] train-rmse:0.471748 test-rmse:0.727424 [13] train-rmse:0.455818 test-rmse:0.706156 [14] train-rmse:0.447443 test-rmse:0.693073 [15] train-rmse:0.443144 test-rmse:0.684812 [16] train-rmse:0.440928 test-rmse:0.679889 [17] train-rmse:0.439771 test-rmse:0.676371 [18] train-rmse:0.439175 test-rmse:0.674131 [19] train-rmse:0.438829 test-rmse:0.672496 [20] train-rmse:0.438647 test-rmse:0.671658 [1] train-rmse:8.429544 test-rmse:8.316669 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.913065 test-rmse:5.803214 [3] train-rmse:4.155122 test-rmse:4.049647 [4] train-rmse:2.929546 test-rmse:2.808874 [5] train-rmse:2.078239 test-rmse:1.953477 [6] train-rmse:1.492094 test-rmse:1.376066 [7] train-rmse:1.094977 test-rmse:0.997863 [8] train-rmse:0.833120 test-rmse:0.770711 [9] train-rmse:0.668160 test-rmse:0.647626 [10] train-rmse:0.569452 test-rmse:0.592804 [11] train-rmse:0.513893 test-rmse:0.574602 [12] train-rmse:0.483799 test-rmse:0.572874 [13] train-rmse:0.468032 test-rmse:0.577515 [14] train-rmse:0.459966 test-rmse:0.583583 [15] train-rmse:0.455866 test-rmse:0.589249 Stopping. Best iteration: [12] train-rmse:0.483799 test-rmse:0.572874 [1] train-rmse:8.411026 test-rmse:8.449055 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.901569 test-rmse:5.938712 [3] train-rmse:4.149264 test-rmse:4.184974 [4] train-rmse:2.928716 test-rmse:2.945583 [5] train-rmse:2.082651 test-rmse:2.082201 [6] train-rmse:1.502124 test-rmse:1.486189 [7] train-rmse:1.111133 test-rmse:1.085086 [8] train-rmse:0.855237 test-rmse:0.819402 [9] train-rmse:0.696274 test-rmse:0.649386 [10] train-rmse:0.601858 test-rmse:0.548534 [11] train-rmse:0.549250 test-rmse:0.490405 [12] train-rmse:0.520973 test-rmse:0.460296 [13] train-rmse:0.506323 test-rmse:0.446579 [14] train-rmse:0.498735 test-rmse:0.440155 [15] train-rmse:0.494862 test-rmse:0.438907 [16] train-rmse:0.492728 test-rmse:0.437128 [17] train-rmse:0.491508 test-rmse:0.437376 [18] train-rmse:0.490799 test-rmse:0.437999 [19] train-rmse:0.490394 test-rmse:0.438829 Stopping. Best iteration: [16] train-rmse:0.492728 test-rmse:0.437128
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.911283 test-rmse:8.804555 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.261204 test-rmse:6.162061 [3] train-rmse:4.412914 test-rmse:4.322649 [4] train-rmse:3.129190 test-rmse:3.082907 [5] train-rmse:2.244666 test-rmse:2.228294 [6] train-rmse:1.643766 test-rmse:1.657706 [7] train-rmse:1.246746 test-rmse:1.288564 [8] train-rmse:0.995102 test-rmse:1.057303 [9] train-rmse:0.844252 test-rmse:0.923828 [10] train-rmse:0.758979 test-rmse:0.849902 [11] train-rmse:0.712999 test-rmse:0.811804 [12] train-rmse:0.689168 test-rmse:0.791080 [13] train-rmse:0.677039 test-rmse:0.782572 [14] train-rmse:0.670956 test-rmse:0.777855 [15] train-rmse:0.667908 test-rmse:0.775728 [16] train-rmse:0.666392 test-rmse:0.774694 [17] train-rmse:0.665605 test-rmse:0.774793 [18] train-rmse:0.665210 test-rmse:0.774309 [19] train-rmse:0.665008 test-rmse:0.774244 [20] train-rmse:0.664898 test-rmse:0.774599 [1] train-rmse:8.878911 test-rmse:9.012408 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.238952 test-rmse:6.359828 [3] train-rmse:4.398570 test-rmse:4.501614 [4] train-rmse:3.121231 test-rmse:3.209762 [5] train-rmse:2.241521 test-rmse:2.289532 [6] train-rmse:1.645504 test-rmse:1.679771 [7] train-rmse:1.252718 test-rmse:1.259294 [8] train-rmse:1.004448 test-rmse:0.978858 [9] train-rmse:0.856182 test-rmse:0.815718 [10] train-rmse:0.772583 test-rmse:0.718340 [11] train-rmse:0.727787 test-rmse:0.665877 [12] train-rmse:0.704555 test-rmse:0.636168 [13] train-rmse:0.692734 test-rmse:0.621826 [14] train-rmse:0.686770 test-rmse:0.614690 [15] train-rmse:0.683806 test-rmse:0.611375 [16] train-rmse:0.682296 test-rmse:0.609858 [17] train-rmse:0.681535 test-rmse:0.609327 [18] train-rmse:0.681147 test-rmse:0.609034 [19] train-rmse:0.680948 test-rmse:0.608916 [20] train-rmse:0.680844 test-rmse:0.608891 [1] train-rmse:8.831444 test-rmse:9.273446 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.198185 test-rmse:6.727879 [3] train-rmse:4.359586 test-rmse:4.938303 [4] train-rmse:3.079426 test-rmse:3.715644 [5] train-rmse:2.192338 test-rmse:2.923641 [6] train-rmse:1.583477 test-rmse:2.400524 [7] train-rmse:1.173978 test-rmse:2.065872 [8] train-rmse:0.905535 test-rmse:1.858532 [9] train-rmse:0.738360 test-rmse:1.724139 [10] train-rmse:0.639908 test-rmse:1.645771 [11] train-rmse:0.584991 test-rmse:1.597028 [12] train-rmse:0.555736 test-rmse:1.565867 [13] train-rmse:0.540636 test-rmse:1.544601 [14] train-rmse:0.532875 test-rmse:1.530817 [15] train-rmse:0.528947 test-rmse:1.521722 [16] train-rmse:0.526955 test-rmse:1.512106 [17] train-rmse:0.525946 test-rmse:1.505312 [18] train-rmse:0.525430 test-rmse:1.500434 [19] train-rmse:0.525159 test-rmse:1.499824 [20] train-rmse:0.525017 test-rmse:1.499476 [1] train-rmse:8.945655 test-rmse:8.567410 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.283908 test-rmse:5.912596 [3] train-rmse:4.426680 test-rmse:4.069900 [4] train-rmse:3.135233 test-rmse:2.808627 [5] train-rmse:2.244017 test-rmse:1.987140 [6] train-rmse:1.637170 test-rmse:1.453141 [7] train-rmse:1.234102 test-rmse:1.135342 [8] train-rmse:0.976841 test-rmse:0.968077 [9] train-rmse:0.821093 test-rmse:0.897189 [10] train-rmse:0.731910 test-rmse:0.876480 [11] train-rmse:0.683602 test-rmse:0.877684 [12] train-rmse:0.658515 test-rmse:0.885380 [13] train-rmse:0.645756 test-rmse:0.894056 Stopping. Best iteration: [10] train-rmse:0.731910 test-rmse:0.876480 [1] train-rmse:8.919353 test-rmse:8.721789 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.264048 test-rmse:6.080637 [3] train-rmse:4.411657 test-rmse:4.268438 [4] train-rmse:3.124156 test-rmse:3.024687 [5] train-rmse:2.235371 test-rmse:2.191426 [6] train-rmse:1.630177 test-rmse:1.654078 [7] train-rmse:1.228157 test-rmse:1.323118 [8] train-rmse:0.971293 test-rmse:1.132168 [9] train-rmse:0.815808 test-rmse:1.031778 [10] train-rmse:0.727124 test-rmse:0.982309 [11] train-rmse:0.679235 test-rmse:0.959702 [12] train-rmse:0.654277 test-rmse:0.949929 [13] train-rmse:0.641562 test-rmse:0.946003 [14] train-rmse:0.635182 test-rmse:0.945048 [15] train-rmse:0.631963 test-rmse:0.945077 [16] train-rmse:0.630348 test-rmse:0.945461 [17] train-rmse:0.629533 test-rmse:0.945904 Stopping. Best iteration: [14] train-rmse:0.635182 test-rmse:0.945048
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:9.334619 test-rmse:9.691025 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.573302 test-rmse:6.950514 [3] train-rmse:4.647714 test-rmse:5.017888 [4] train-rmse:3.310369 test-rmse:3.685518 [5] train-rmse:2.389721 test-rmse:2.765725 [6] train-rmse:1.760341 test-rmse:2.124024 [7] train-rmse:1.343923 test-rmse:1.696063 [8] train-rmse:1.077168 test-rmse:1.414372 [9] train-rmse:0.911895 test-rmse:1.232840 [10] train-rmse:0.809892 test-rmse:1.108398 [11] train-rmse:0.749641 test-rmse:1.027100 [12] train-rmse:0.714642 test-rmse:0.973292 [13] train-rmse:0.685916 test-rmse:0.934326 [14] train-rmse:0.661050 test-rmse:0.918371 [15] train-rmse:0.645794 test-rmse:0.906457 [16] train-rmse:0.639210 test-rmse:0.897512 [17] train-rmse:0.632286 test-rmse:0.893469 [18] train-rmse:0.628314 test-rmse:0.891724 [19] train-rmse:0.623381 test-rmse:0.890070 [20] train-rmse:0.621131 test-rmse:0.891209 [1] train-rmse:9.328274 test-rmse:9.788075 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.565904 test-rmse:7.045877 [3] train-rmse:4.637515 test-rmse:5.157988 [4] train-rmse:3.297447 test-rmse:3.855983 [5] train-rmse:2.371859 test-rmse:2.983742 [6] train-rmse:1.740116 test-rmse:2.387821 [7] train-rmse:1.316751 test-rmse:2.012995 [8] train-rmse:1.044754 test-rmse:1.778029 [9] train-rmse:0.877008 test-rmse:1.637978 [10] train-rmse:0.768496 test-rmse:1.553162 [11] train-rmse:0.709069 test-rmse:1.498376 [12] train-rmse:0.671863 test-rmse:1.468917 [13] train-rmse:0.640075 test-rmse:1.453207 [14] train-rmse:0.626049 test-rmse:1.434605 [15] train-rmse:0.616830 test-rmse:1.423682 [16] train-rmse:0.607986 test-rmse:1.416512 [17] train-rmse:0.603792 test-rmse:1.411543 [18] train-rmse:0.593168 test-rmse:1.406738 [19] train-rmse:0.588075 test-rmse:1.405815 [20] train-rmse:0.583165 test-rmse:1.403184 [1] train-rmse:9.335799 test-rmse:9.884896 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.573852 test-rmse:7.101778 [3] train-rmse:4.645929 test-rmse:5.183604 [4] train-rmse:3.303308 test-rmse:3.856735 [5] train-rmse:2.376934 test-rmse:2.936373 [6] train-rmse:1.746946 test-rmse:2.324975 [7] train-rmse:1.320710 test-rmse:1.923344 [8] train-rmse:1.047057 test-rmse:1.649898 [9] train-rmse:0.874050 test-rmse:1.493172 [10] train-rmse:0.769258 test-rmse:1.402066 [11] train-rmse:0.707717 test-rmse:1.349176 [12] train-rmse:0.673651 test-rmse:1.308503 [13] train-rmse:0.648689 test-rmse:1.291665 [14] train-rmse:0.634759 test-rmse:1.274815 [15] train-rmse:0.618423 test-rmse:1.270678 [16] train-rmse:0.613333 test-rmse:1.265539 [17] train-rmse:0.600063 test-rmse:1.262508 [18] train-rmse:0.596075 test-rmse:1.262538 [19] train-rmse:0.593198 test-rmse:1.256011 [20] train-rmse:0.583823 test-rmse:1.254814 [1] train-rmse:9.416021 test-rmse:9.191758 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.631063 test-rmse:6.419644 [3] train-rmse:4.685687 test-rmse:4.483057 [4] train-rmse:3.333258 test-rmse:3.157723 [5] train-rmse:2.400735 test-rmse:2.261412 [6] train-rmse:1.762954 test-rmse:1.672874 [7] train-rmse:1.339244 test-rmse:1.314063 [8] train-rmse:1.063279 test-rmse:1.106882 [9] train-rmse:0.894113 test-rmse:1.005091 [10] train-rmse:0.793540 test-rmse:0.955712 [11] train-rmse:0.727043 test-rmse:0.942370 [12] train-rmse:0.690327 test-rmse:0.941068 [13] train-rmse:0.672724 test-rmse:0.944524 [14] train-rmse:0.657752 test-rmse:0.952851 [15] train-rmse:0.651450 test-rmse:0.957656 Stopping. Best iteration: [12] train-rmse:0.690327 test-rmse:0.941068 [1] train-rmse:9.435901 test-rmse:8.969103 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.642978 test-rmse:6.207485 [3] train-rmse:4.693714 test-rmse:4.291008 [4] train-rmse:3.340111 test-rmse:2.974144 [5] train-rmse:2.403984 test-rmse:2.092182 [6] train-rmse:1.767821 test-rmse:1.529368 [7] train-rmse:1.340549 test-rmse:1.192090 [8] train-rmse:1.063212 test-rmse:1.022377 [9] train-rmse:0.891719 test-rmse:0.944175 [10] train-rmse:0.789716 test-rmse:0.927945 [11] train-rmse:0.718953 test-rmse:0.923136 [12] train-rmse:0.688032 test-rmse:0.933159 [13] train-rmse:0.653151 test-rmse:0.950096 [14] train-rmse:0.638652 test-rmse:0.954962 Stopping. Best iteration: [11] train-rmse:0.718953 test-rmse:0.923136
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.871769 test-rmse:9.049367 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.222945 test-rmse:6.392780 [3] train-rmse:4.372407 test-rmse:4.543027 [4] train-rmse:3.082842 test-rmse:3.246987 [5] train-rmse:2.188039 test-rmse:2.348459 [6] train-rmse:1.572724 test-rmse:1.728853 [7] train-rmse:1.156482 test-rmse:1.310050 [8] train-rmse:0.882606 test-rmse:1.032196 [9] train-rmse:0.709740 test-rmse:0.852438 [10] train-rmse:0.606243 test-rmse:0.740252 [11] train-rmse:0.547758 test-rmse:0.672580 [12] train-rmse:0.515439 test-rmse:0.632427 [13] train-rmse:0.498576 test-rmse:0.607631 [14] train-rmse:0.488835 test-rmse:0.592289 [15] train-rmse:0.482948 test-rmse:0.583087 [16] train-rmse:0.478321 test-rmse:0.577266 [17] train-rmse:0.477127 test-rmse:0.573627 [18] train-rmse:0.476185 test-rmse:0.571933 [19] train-rmse:0.475536 test-rmse:0.569880 [20] train-rmse:0.473969 test-rmse:0.569213 [1] train-rmse:8.837584 test-rmse:9.253055 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.198849 test-rmse:6.605327 [3] train-rmse:4.355356 test-rmse:4.755630 [4] train-rmse:3.070400 test-rmse:3.459207 [5] train-rmse:2.178630 test-rmse:2.556367 [6] train-rmse:1.564802 test-rmse:1.930935 [7] train-rmse:1.149257 test-rmse:1.501388 [8] train-rmse:0.875249 test-rmse:1.206288 [9] train-rmse:0.701543 test-rmse:1.007097 [10] train-rmse:0.596818 test-rmse:0.873066 [11] train-rmse:0.537486 test-rmse:0.784528 [12] train-rmse:0.504778 test-rmse:0.725539 [13] train-rmse:0.487065 test-rmse:0.686241 [14] train-rmse:0.478066 test-rmse:0.660331 [15] train-rmse:0.472270 test-rmse:0.641592 [16] train-rmse:0.467814 test-rmse:0.629029 [17] train-rmse:0.465355 test-rmse:0.620723 [18] train-rmse:0.464127 test-rmse:0.614714 [19] train-rmse:0.463255 test-rmse:0.611011 [20] train-rmse:0.461783 test-rmse:0.608775 [1] train-rmse:8.885192 test-rmse:8.965597 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.232609 test-rmse:6.296149 [3] train-rmse:4.379749 test-rmse:4.413186 [4] train-rmse:3.088504 test-rmse:3.101133 [5] train-rmse:2.192707 test-rmse:2.192520 [6] train-rmse:1.576788 test-rmse:1.570400 [7] train-rmse:1.160388 test-rmse:1.155551 [8] train-rmse:0.886741 test-rmse:0.884350 [9] train-rmse:0.712908 test-rmse:0.719665 [10] train-rmse:0.609554 test-rmse:0.628319 [11] train-rmse:0.551313 test-rmse:0.582625 [12] train-rmse:0.518557 test-rmse:0.562662 [13] train-rmse:0.501911 test-rmse:0.553595 [14] train-rmse:0.491968 test-rmse:0.551601 [15] train-rmse:0.486504 test-rmse:0.551430 [16] train-rmse:0.482997 test-rmse:0.552631 [17] train-rmse:0.480651 test-rmse:0.552924 [18] train-rmse:0.479191 test-rmse:0.554034 Stopping. Best iteration: [15] train-rmse:0.486504 test-rmse:0.551430 [1] train-rmse:8.949281 test-rmse:8.552266 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.277280 test-rmse:5.902604 [3] train-rmse:4.410951 test-rmse:4.060994 [4] train-rmse:3.110106 test-rmse:2.789930 [5] train-rmse:2.207698 test-rmse:1.917758 [6] train-rmse:1.586963 test-rmse:1.339148 [7] train-rmse:1.167017 test-rmse:0.979877 [8] train-rmse:0.891106 test-rmse:0.781173 [9] train-rmse:0.716618 test-rmse:0.691791 [10] train-rmse:0.611968 test-rmse:0.665622 [11] train-rmse:0.552954 test-rmse:0.667164 [12] train-rmse:0.520519 test-rmse:0.677249 [13] train-rmse:0.503106 test-rmse:0.690070 Stopping. Best iteration: [10] train-rmse:0.611968 test-rmse:0.665622 [1] train-rmse:8.942601 test-rmse:8.588408 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.271884 test-rmse:5.939037 [3] train-rmse:4.406003 test-rmse:4.106848 [4] train-rmse:3.105184 test-rmse:2.836034 [5] train-rmse:2.201844 test-rmse:1.975791 [6] train-rmse:1.579955 test-rmse:1.409410 [7] train-rmse:1.158152 test-rmse:1.060213 [8] train-rmse:0.879954 test-rmse:0.870344 [9] train-rmse:0.702847 test-rmse:0.783678 [10] train-rmse:0.594871 test-rmse:0.754072 [11] train-rmse:0.534560 test-rmse:0.751514 [12] train-rmse:0.500653 test-rmse:0.758712 [13] train-rmse:0.483374 test-rmse:0.767826 [14] train-rmse:0.472129 test-rmse:0.776673 Stopping. Best iteration: [11] train-rmse:0.534560 test-rmse:0.751514
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.977412 test-rmse:9.095025 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.297731 test-rmse:6.426375 [3] train-rmse:4.426542 test-rmse:4.565155 [4] train-rmse:3.123334 test-rmse:3.279535 [5] train-rmse:2.219755 test-rmse:2.407603 [6] train-rmse:1.599660 test-rmse:1.817239 [7] train-rmse:1.180725 test-rmse:1.439994 [8] train-rmse:0.906420 test-rmse:1.202866 [9] train-rmse:0.731669 test-rmse:1.065964 [10] train-rmse:0.627325 test-rmse:0.986660 [11] train-rmse:0.567608 test-rmse:0.941814 [12] train-rmse:0.535044 test-rmse:0.917421 [13] train-rmse:0.517234 test-rmse:0.902531 [14] train-rmse:0.506370 test-rmse:0.895619 [15] train-rmse:0.501051 test-rmse:0.890558 [16] train-rmse:0.498709 test-rmse:0.887659 [17] train-rmse:0.496094 test-rmse:0.886240 [18] train-rmse:0.494859 test-rmse:0.884845 [19] train-rmse:0.493485 test-rmse:0.884369 [20] train-rmse:0.490355 test-rmse:0.885323 [1] train-rmse:8.970402 test-rmse:9.162219 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.294478 test-rmse:6.486244 [3] train-rmse:4.426638 test-rmse:4.611622 [4] train-rmse:3.126463 test-rmse:3.308144 [5] train-rmse:2.225873 test-rmse:2.408658 [6] train-rmse:1.608827 test-rmse:1.791699 [7] train-rmse:1.194598 test-rmse:1.374848 [8] train-rmse:0.925051 test-rmse:1.102451 [9] train-rmse:0.754014 test-rmse:0.924061 [10] train-rmse:0.653191 test-rmse:0.815345 [11] train-rmse:0.595345 test-rmse:0.750432 [12] train-rmse:0.562273 test-rmse:0.710754 [13] train-rmse:0.544751 test-rmse:0.687314 [14] train-rmse:0.535327 test-rmse:0.672350 [15] train-rmse:0.529024 test-rmse:0.662527 [16] train-rmse:0.526186 test-rmse:0.656910 [17] train-rmse:0.522869 test-rmse:0.653877 [18] train-rmse:0.519452 test-rmse:0.652402 [19] train-rmse:0.518293 test-rmse:0.651179 [20] train-rmse:0.515480 test-rmse:0.649679 [1] train-rmse:8.991560 test-rmse:9.026932 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.309364 test-rmse:6.347545 [3] train-rmse:4.437284 test-rmse:4.478787 [4] train-rmse:3.134243 test-rmse:3.179408 [5] train-rmse:2.232368 test-rmse:2.266924 [6] train-rmse:1.615271 test-rmse:1.658578 [7] train-rmse:1.199937 test-rmse:1.242678 [8] train-rmse:0.928149 test-rmse:0.981059 [9] train-rmse:0.759534 test-rmse:0.829242 [10] train-rmse:0.659669 test-rmse:0.746677 [11] train-rmse:0.602732 test-rmse:0.703627 [12] train-rmse:0.571325 test-rmse:0.686137 [13] train-rmse:0.554150 test-rmse:0.677875 [14] train-rmse:0.544037 test-rmse:0.676260 [15] train-rmse:0.539388 test-rmse:0.675162 [16] train-rmse:0.535099 test-rmse:0.675381 [17] train-rmse:0.532956 test-rmse:0.676280 [18] train-rmse:0.532047 test-rmse:0.676307 Stopping. Best iteration: [15] train-rmse:0.539388 test-rmse:0.675162 [1] train-rmse:9.029188 test-rmse:8.779391 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.335779 test-rmse:6.093065 [3] train-rmse:4.455746 test-rmse:4.225814 [4] train-rmse:3.147157 test-rmse:2.938445 [5] train-rmse:2.241516 test-rmse:2.062734 [6] train-rmse:1.620285 test-rmse:1.488531 [7] train-rmse:1.203111 test-rmse:1.129172 [8] train-rmse:0.929023 test-rmse:0.929611 [9] train-rmse:0.758238 test-rmse:0.833485 [10] train-rmse:0.656945 test-rmse:0.795632 [11] train-rmse:0.599202 test-rmse:0.787507 [12] train-rmse:0.567002 test-rmse:0.790164 [13] train-rmse:0.547093 test-rmse:0.798280 [14] train-rmse:0.535947 test-rmse:0.805620 Stopping. Best iteration: [11] train-rmse:0.599202 test-rmse:0.787507 [1] train-rmse:9.021622 test-rmse:8.838467 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.330355 test-rmse:6.151177 [3] train-rmse:4.452057 test-rmse:4.288078 [4] train-rmse:3.144822 test-rmse:2.995605 [5] train-rmse:2.240338 test-rmse:2.108055 [6] train-rmse:1.621285 test-rmse:1.510670 [7] train-rmse:1.205714 test-rmse:1.125226 [8] train-rmse:0.933332 test-rmse:0.895896 [9] train-rmse:0.763588 test-rmse:0.766842 [10] train-rmse:0.663323 test-rmse:0.704284 [11] train-rmse:0.606769 test-rmse:0.677971 [12] train-rmse:0.575585 test-rmse:0.670176 [13] train-rmse:0.559350 test-rmse:0.670468 [14] train-rmse:0.549776 test-rmse:0.672531 [15] train-rmse:0.543526 test-rmse:0.675179 Stopping. Best iteration: [12] train-rmse:0.575585 test-rmse:0.670176
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.573519 test-rmse:8.303055 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.051747 test-rmse:5.794902 [3] train-rmse:4.288547 test-rmse:4.052012 [4] train-rmse:3.046811 test-rmse:2.856314 [5] train-rmse:2.178181 test-rmse:2.025220 [6] train-rmse:1.571218 test-rmse:1.463972 [7] train-rmse:1.153838 test-rmse:1.099815 [8] train-rmse:0.866909 test-rmse:0.903848 [9] train-rmse:0.672298 test-rmse:0.777684 [10] train-rmse:0.528035 test-rmse:0.709474 [11] train-rmse:0.432202 test-rmse:0.690254 [12] train-rmse:0.362951 test-rmse:0.683508 [13] train-rmse:0.320304 test-rmse:0.682542 [14] train-rmse:0.287393 test-rmse:0.683023 [15] train-rmse:0.268639 test-rmse:0.686247 [16] train-rmse:0.243872 test-rmse:0.686318 Stopping. Best iteration: [13] train-rmse:0.320304 test-rmse:0.682542 [1] train-rmse:8.662565 test-rmse:7.811053 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.115185 test-rmse:5.253593 [3] train-rmse:4.330695 test-rmse:3.693125 [4] train-rmse:3.079269 test-rmse:2.427833 [5] train-rmse:2.202468 test-rmse:1.694694 [6] train-rmse:1.592544 test-rmse:1.200259 [7] train-rmse:1.173470 test-rmse:0.815787 [8] train-rmse:0.884639 test-rmse:0.590276 [9] train-rmse:0.693650 test-rmse:0.450817 [10] train-rmse:0.552802 test-rmse:0.370832 [11] train-rmse:0.458401 test-rmse:0.334770 [12] train-rmse:0.394404 test-rmse:0.309782 [13] train-rmse:0.350724 test-rmse:0.297146 [14] train-rmse:0.322379 test-rmse:0.289028 [15] train-rmse:0.301459 test-rmse:0.275953 [16] train-rmse:0.284873 test-rmse:0.275963 [17] train-rmse:0.266791 test-rmse:0.276835 [18] train-rmse:0.253028 test-rmse:0.278469 Stopping. Best iteration: [15] train-rmse:0.301459 test-rmse:0.275953 [1] train-rmse:8.493035 test-rmse:8.884759 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.000110 test-rmse:6.379416 [3] train-rmse:4.249930 test-rmse:4.415771 [4] train-rmse:3.018740 test-rmse:3.032956 [5] train-rmse:2.156034 test-rmse:2.063747 [6] train-rmse:1.555893 test-rmse:1.418786 [7] train-rmse:1.143873 test-rmse:0.958402 [8] train-rmse:0.864256 test-rmse:0.658107 [9] train-rmse:0.675724 test-rmse:0.478038 [10] train-rmse:0.535475 test-rmse:0.383648 [11] train-rmse:0.443073 test-rmse:0.351210 [12] train-rmse:0.378490 test-rmse:0.361915 [13] train-rmse:0.336472 test-rmse:0.390987 [14] train-rmse:0.308103 test-rmse:0.423164 Stopping. Best iteration: [11] train-rmse:0.443073 test-rmse:0.351210 [1] train-rmse:8.529848 test-rmse:8.698165 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.021867 test-rmse:6.196264 [3] train-rmse:4.261803 test-rmse:4.536107 [4] train-rmse:3.023820 test-rmse:3.373971 [5] train-rmse:2.155186 test-rmse:2.542917 [6] train-rmse:1.549782 test-rmse:1.968575 [7] train-rmse:1.132527 test-rmse:1.578082 [8] train-rmse:0.847138 test-rmse:1.297174 [9] train-rmse:0.653720 test-rmse:1.105003 [10] train-rmse:0.513171 test-rmse:1.002796 [11] train-rmse:0.418983 test-rmse:0.946621 [12] train-rmse:0.357407 test-rmse:0.909473 [13] train-rmse:0.306556 test-rmse:0.861613 [14] train-rmse:0.271658 test-rmse:0.833673 [15] train-rmse:0.249657 test-rmse:0.816200 [16] train-rmse:0.234351 test-rmse:0.816201 [17] train-rmse:0.219526 test-rmse:0.810445 [18] train-rmse:0.210285 test-rmse:0.817574 [19] train-rmse:0.197638 test-rmse:0.810230 [20] train-rmse:0.190916 test-rmse:0.809364 [1] train-rmse:8.478056 test-rmse:8.939548 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.985711 test-rmse:6.446171 [3] train-rmse:4.238306 test-rmse:4.563408 [4] train-rmse:3.008878 test-rmse:3.243598 [5] train-rmse:2.145991 test-rmse:2.314073 [6] train-rmse:1.543656 test-rmse:1.672448 [7] train-rmse:1.127772 test-rmse:1.253839 [8] train-rmse:0.842518 test-rmse:0.966556 [9] train-rmse:0.646904 test-rmse:0.800800 [10] train-rmse:0.514093 test-rmse:0.702210 [11] train-rmse:0.425199 test-rmse:0.669449 [12] train-rmse:0.363516 test-rmse:0.666174 [13] train-rmse:0.322519 test-rmse:0.679003 [14] train-rmse:0.292671 test-rmse:0.679198 [15] train-rmse:0.273791 test-rmse:0.687383 Stopping. Best iteration: [12] train-rmse:0.363516 test-rmse:0.666174
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.341174 test-rmse:8.021919 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.908005 test-rmse:5.590281 [3] train-rmse:4.190333 test-rmse:3.875126 [4] train-rmse:2.980085 test-rmse:2.669165 [5] train-rmse:2.130573 test-rmse:1.827211 [6] train-rmse:1.538592 test-rmse:1.248906 [7] train-rmse:1.130200 test-rmse:0.850389 [8] train-rmse:0.852099 test-rmse:0.621709 [9] train-rmse:0.660633 test-rmse:0.453550 [10] train-rmse:0.532564 test-rmse:0.388788 [11] train-rmse:0.450300 test-rmse:0.390595 [12] train-rmse:0.399834 test-rmse:0.419031 [13] train-rmse:0.370182 test-rmse:0.451829 Stopping. Best iteration: [10] train-rmse:0.532564 test-rmse:0.388788 [1] train-rmse:8.326715 test-rmse:8.156098 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.899113 test-rmse:5.725345 [3] train-rmse:4.185922 test-rmse:4.007790 [4] train-rmse:2.979598 test-rmse:2.795500 [5] train-rmse:2.133885 test-rmse:1.941815 [6] train-rmse:1.545933 test-rmse:1.343651 [7] train-rmse:1.143454 test-rmse:0.929108 [8] train-rmse:0.864110 test-rmse:0.667253 [9] train-rmse:0.673583 test-rmse:0.498227 [10] train-rmse:0.547888 test-rmse:0.393507 [11] train-rmse:0.468911 test-rmse:0.337422 [12] train-rmse:0.421652 test-rmse:0.312606 [13] train-rmse:0.394578 test-rmse:0.304205 [14] train-rmse:0.379603 test-rmse:0.303635 [15] train-rmse:0.371485 test-rmse:0.305960 [16] train-rmse:0.367143 test-rmse:0.309021 [17] train-rmse:0.364838 test-rmse:0.311838 Stopping. Best iteration: [14] train-rmse:0.379603 test-rmse:0.303635 [1] train-rmse:8.305666 test-rmse:8.268465 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.884121 test-rmse:5.845323 [3] train-rmse:4.175172 test-rmse:4.134151 [4] train-rmse:2.971790 test-rmse:2.927708 [5] train-rmse:2.128078 test-rmse:2.079857 [6] train-rmse:1.541436 test-rmse:1.487797 [7] train-rmse:1.139754 test-rmse:1.079418 [8] train-rmse:0.860195 test-rmse:0.826783 [9] train-rmse:0.668646 test-rmse:0.661587 [10] train-rmse:0.541959 test-rmse:0.554288 [11] train-rmse:0.461913 test-rmse:0.489530 [12] train-rmse:0.413666 test-rmse:0.450415 [13] train-rmse:0.385947 test-rmse:0.427826 [14] train-rmse:0.370549 test-rmse:0.415040 [15] train-rmse:0.362173 test-rmse:0.407496 [16] train-rmse:0.357686 test-rmse:0.403074 [17] train-rmse:0.355298 test-rmse:0.400455 [18] train-rmse:0.354031 test-rmse:0.398812 [19] train-rmse:0.353360 test-rmse:0.397776 [20] train-rmse:0.353005 test-rmse:0.397114 [1] train-rmse:8.219663 test-rmse:8.786036 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.821537 test-rmse:6.384151 [3] train-rmse:4.128438 test-rmse:4.685694 [4] train-rmse:2.935274 test-rmse:3.484896 [5] train-rmse:2.097440 test-rmse:2.636220 [6] train-rmse:1.513177 test-rmse:2.036705 [7] train-rmse:1.110993 test-rmse:1.613487 [8] train-rmse:0.840410 test-rmse:1.314971 [9] train-rmse:0.652735 test-rmse:1.071530 [10] train-rmse:0.526973 test-rmse:0.897086 [11] train-rmse:0.445792 test-rmse:0.773875 [12] train-rmse:0.395875 test-rmse:0.686410 [13] train-rmse:0.366408 test-rmse:0.625722 [14] train-rmse:0.349612 test-rmse:0.583142 [15] train-rmse:0.340253 test-rmse:0.553883 [16] train-rmse:0.335101 test-rmse:0.533358 [17] train-rmse:0.332289 test-rmse:0.519102 [18] train-rmse:0.330758 test-rmse:0.509253 [19] train-rmse:0.329924 test-rmse:0.502278 [20] train-rmse:0.329470 test-rmse:0.497429 [1] train-rmse:8.317634 test-rmse:8.179597 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.890654 test-rmse:5.757075 [3] train-rmse:4.177073 test-rmse:4.049969 [4] train-rmse:2.969322 test-rmse:2.851678 [5] train-rmse:2.121032 test-rmse:2.017232 [6] train-rmse:1.529196 test-rmse:1.445489 [7] train-rmse:1.121441 test-rmse:1.065989 [8] train-rmse:0.845375 test-rmse:0.825430 [9] train-rmse:0.660321 test-rmse:0.653876 [10] train-rmse:0.539415 test-rmse:0.588571 [11] train-rmse:0.464122 test-rmse:0.587392 [12] train-rmse:0.419333 test-rmse:0.613285 [13] train-rmse:0.393890 test-rmse:0.645601 [14] train-rmse:0.379876 test-rmse:0.675732 Stopping. Best iteration: [11] train-rmse:0.464122 test-rmse:0.587392
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.466039 test-rmse:8.465558 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.938439 test-rmse:5.943923 [3] train-rmse:4.170215 test-rmse:4.184163 [4] train-rmse:2.935223 test-rmse:2.961088 [5] train-rmse:2.075450 test-rmse:2.117832 [6] train-rmse:1.479147 test-rmse:1.529664 [7] train-rmse:1.067783 test-rmse:1.138269 [8] train-rmse:0.788610 test-rmse:0.896621 [9] train-rmse:0.604497 test-rmse:0.758869 [10] train-rmse:0.488218 test-rmse:0.687078 [11] train-rmse:0.418250 test-rmse:0.659526 [12] train-rmse:0.379554 test-rmse:0.648861 [13] train-rmse:0.357893 test-rmse:0.648643 [14] train-rmse:0.346613 test-rmse:0.651781 [15] train-rmse:0.340812 test-rmse:0.655947 [16] train-rmse:0.337858 test-rmse:0.659658 Stopping. Best iteration: [13] train-rmse:0.357893 test-rmse:0.648643 [1] train-rmse:8.483954 test-rmse:8.351552 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.950312 test-rmse:5.823750 [3] train-rmse:4.177577 test-rmse:4.059524 [4] train-rmse:2.939030 test-rmse:2.833413 [5] train-rmse:2.076218 test-rmse:1.988765 [6] train-rmse:1.478617 test-rmse:1.417357 [7] train-rmse:1.066680 test-rmse:1.106113 [8] train-rmse:0.787205 test-rmse:0.906315 [9] train-rmse:0.602983 test-rmse:0.783005 [10] train-rmse:0.487450 test-rmse:0.691718 [11] train-rmse:0.417916 test-rmse:0.654905 [12] train-rmse:0.378812 test-rmse:0.634090 [13] train-rmse:0.358223 test-rmse:0.615005 [14] train-rmse:0.347236 test-rmse:0.609743 [15] train-rmse:0.341631 test-rmse:0.607347 [16] train-rmse:0.338805 test-rmse:0.605557 [17] train-rmse:0.337387 test-rmse:0.604788 [18] train-rmse:0.336678 test-rmse:0.604091 [19] train-rmse:0.336323 test-rmse:0.603810 [20] train-rmse:0.336146 test-rmse:0.603501 [1] train-rmse:8.455255 test-rmse:8.537645 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.930309 test-rmse:6.019253 [3] train-rmse:4.163712 test-rmse:4.261814 [4] train-rmse:2.929527 test-rmse:3.040253 [5] train-rmse:2.068790 test-rmse:2.176258 [6] train-rmse:1.471069 test-rmse:1.591244 [7] train-rmse:1.059937 test-rmse:1.206887 [8] train-rmse:0.781745 test-rmse:0.970875 [9] train-rmse:0.599208 test-rmse:0.827430 [10] train-rmse:0.484150 test-rmse:0.748456 [11] train-rmse:0.415725 test-rmse:0.706975 [12] train-rmse:0.377253 test-rmse:0.687257 [13] train-rmse:0.356659 test-rmse:0.678030 [14] train-rmse:0.345995 test-rmse:0.672365 [15] train-rmse:0.340570 test-rmse:0.669423 [16] train-rmse:0.337834 test-rmse:0.667725 [17] train-rmse:0.336462 test-rmse:0.666732 [18] train-rmse:0.335775 test-rmse:0.666102 [19] train-rmse:0.335431 test-rmse:0.665668 [20] train-rmse:0.335259 test-rmse:0.665317 [1] train-rmse:8.485000 test-rmse:8.354339 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.950431 test-rmse:5.827547 [3] train-rmse:4.176788 test-rmse:4.065189 [4] train-rmse:2.937238 test-rmse:2.842042 [5] train-rmse:2.073215 test-rmse:2.001759 [6] train-rmse:1.473377 test-rmse:1.449172 [7] train-rmse:1.060515 test-rmse:1.088188 [8] train-rmse:0.780689 test-rmse:0.852936 [9] train-rmse:0.593332 test-rmse:0.735616 [10] train-rmse:0.473362 test-rmse:0.673445 [11] train-rmse:0.400475 test-rmse:0.643072 [12] train-rmse:0.358700 test-rmse:0.629386 [13] train-rmse:0.335956 test-rmse:0.623843 [14] train-rmse:0.324025 test-rmse:0.622027 [15] train-rmse:0.317908 test-rmse:0.621792 [16] train-rmse:0.314811 test-rmse:0.622142 [17] train-rmse:0.313253 test-rmse:0.622614 [18] train-rmse:0.312472 test-rmse:0.622974 Stopping. Best iteration: [15] train-rmse:0.317908 test-rmse:0.621792 [1] train-rmse:8.460879 test-rmse:8.501479 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:5.934252 test-rmse:5.978778 [3] train-rmse:4.166475 test-rmse:4.216527 [4] train-rmse:2.931465 test-rmse:2.989214 [5] train-rmse:2.071220 test-rmse:2.139495 [6] train-rmse:1.475247 test-rmse:1.549791 [7] train-rmse:1.065545 test-rmse:1.154409 [8] train-rmse:0.789092 test-rmse:0.896689 [9] train-rmse:0.605388 test-rmse:0.734859 [10] train-rmse:0.490059 test-rmse:0.637976 [11] train-rmse:0.421206 test-rmse:0.582489 [12] train-rmse:0.382419 test-rmse:0.551738 [13] train-rmse:0.361602 test-rmse:0.534957 [14] train-rmse:0.350796 test-rmse:0.525798 [15] train-rmse:0.345297 test-rmse:0.520736 [16] train-rmse:0.342529 test-rmse:0.517880 [17] train-rmse:0.341144 test-rmse:0.515881 [18] train-rmse:0.340452 test-rmse:0.514897 [19] train-rmse:0.340107 test-rmse:0.514160 [20] train-rmse:0.339935 test-rmse:0.513648
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.860887 test-rmse:9.419149 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.250703 test-rmse:6.827483 [3] train-rmse:4.433660 test-rmse:5.033203 [4] train-rmse:3.169367 test-rmse:3.799752 [5] train-rmse:2.294868 test-rmse:2.941979 [6] train-rmse:1.698965 test-rmse:2.357342 [7] train-rmse:1.302652 test-rmse:1.987617 [8] train-rmse:1.044121 test-rmse:1.755374 [9] train-rmse:0.876201 test-rmse:1.597521 [10] train-rmse:0.769597 test-rmse:1.501276 [11] train-rmse:0.694936 test-rmse:1.445897 [12] train-rmse:0.649315 test-rmse:1.426523 [13] train-rmse:0.620367 test-rmse:1.406340 [14] train-rmse:0.598720 test-rmse:1.399132 [15] train-rmse:0.584096 test-rmse:1.392344 [16] train-rmse:0.575342 test-rmse:1.385149 [17] train-rmse:0.555039 test-rmse:1.383729 [18] train-rmse:0.551288 test-rmse:1.380048 [19] train-rmse:0.541712 test-rmse:1.386292 [20] train-rmse:0.536109 test-rmse:1.387796 [1] train-rmse:8.937187 test-rmse:8.678488 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.305332 test-rmse:6.048220 [3] train-rmse:4.473530 test-rmse:4.219601 [4] train-rmse:3.200148 test-rmse:2.983839 [5] train-rmse:2.321718 test-rmse:2.132841 [6] train-rmse:1.721869 test-rmse:1.578593 [7] train-rmse:1.325198 test-rmse:1.212714 [8] train-rmse:1.066280 test-rmse:0.986651 [9] train-rmse:0.893803 test-rmse:0.861413 [10] train-rmse:0.783542 test-rmse:0.793798 [11] train-rmse:0.719188 test-rmse:0.766683 [12] train-rmse:0.677534 test-rmse:0.746004 [13] train-rmse:0.644730 test-rmse:0.737338 [14] train-rmse:0.609831 test-rmse:0.737486 [15] train-rmse:0.588362 test-rmse:0.748371 [16] train-rmse:0.576791 test-rmse:0.747107 Stopping. Best iteration: [13] train-rmse:0.644730 test-rmse:0.737338 [1] train-rmse:8.933369 test-rmse:8.750484 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.299910 test-rmse:6.129613 [3] train-rmse:4.465929 test-rmse:4.314132 [4] train-rmse:3.190187 test-rmse:3.054228 [5] train-rmse:2.305255 test-rmse:2.217621 [6] train-rmse:1.700880 test-rmse:1.662229 [7] train-rmse:1.293358 test-rmse:1.311413 [8] train-rmse:1.022582 test-rmse:1.112365 [9] train-rmse:0.845279 test-rmse:1.027032 [10] train-rmse:0.724640 test-rmse:0.986519 [11] train-rmse:0.643328 test-rmse:0.972372 [12] train-rmse:0.602898 test-rmse:0.967221 [13] train-rmse:0.570679 test-rmse:0.966120 [14] train-rmse:0.547691 test-rmse:0.974442 [15] train-rmse:0.535230 test-rmse:0.978298 [16] train-rmse:0.527875 test-rmse:0.979456 Stopping. Best iteration: [13] train-rmse:0.570679 test-rmse:0.966120 [1] train-rmse:8.880458 test-rmse:8.999302 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.263663 test-rmse:6.399773 [3] train-rmse:4.437692 test-rmse:4.586054 [4] train-rmse:3.165168 test-rmse:3.348353 [5] train-rmse:2.284571 test-rmse:2.519633 [6] train-rmse:1.681803 test-rmse:1.969879 [7] train-rmse:1.277291 test-rmse:1.631227 [8] train-rmse:1.006181 test-rmse:1.468726 [9] train-rmse:0.831193 test-rmse:1.374274 [10] train-rmse:0.721030 test-rmse:1.323056 [11] train-rmse:0.650895 test-rmse:1.301530 [12] train-rmse:0.599898 test-rmse:1.296247 [13] train-rmse:0.571703 test-rmse:1.286805 [14] train-rmse:0.546839 test-rmse:1.288851 [15] train-rmse:0.534970 test-rmse:1.282930 [16] train-rmse:0.525498 test-rmse:1.281086 [17] train-rmse:0.514492 test-rmse:1.288367 [18] train-rmse:0.508881 test-rmse:1.288895 [19] train-rmse:0.494483 test-rmse:1.292425 Stopping. Best iteration: [16] train-rmse:0.525498 test-rmse:1.281086 [1] train-rmse:8.882510 test-rmse:9.108496 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.265134 test-rmse:6.516903 [3] train-rmse:4.442749 test-rmse:4.728793 [4] train-rmse:3.175544 test-rmse:3.478996 [5] train-rmse:2.299533 test-rmse:2.610101 [6] train-rmse:1.702312 test-rmse:2.054795 [7] train-rmse:1.299836 test-rmse:1.738892 [8] train-rmse:1.034501 test-rmse:1.584195 [9] train-rmse:0.856043 test-rmse:1.487033 [10] train-rmse:0.747653 test-rmse:1.421922 [11] train-rmse:0.680277 test-rmse:1.370681 [12] train-rmse:0.629852 test-rmse:1.365252 [13] train-rmse:0.594746 test-rmse:1.349734 [14] train-rmse:0.577405 test-rmse:1.340600 [15] train-rmse:0.553296 test-rmse:1.341183 [16] train-rmse:0.536677 test-rmse:1.341753 [17] train-rmse:0.523726 test-rmse:1.340961 Stopping. Best iteration: [14] train-rmse:0.577405 test-rmse:1.340600
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.780199 test-rmse:8.720125 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.154999 test-rmse:6.105194 [3] train-rmse:4.319520 test-rmse:4.271056 [4] train-rmse:3.037515 test-rmse:2.993212 [5] train-rmse:2.144497 test-rmse:2.103827 [6] train-rmse:1.525852 test-rmse:1.490063 [7] train-rmse:1.101591 test-rmse:1.079584 [8] train-rmse:0.815941 test-rmse:0.806591 [9] train-rmse:0.629491 test-rmse:0.634687 [10] train-rmse:0.511265 test-rmse:0.533876 [11] train-rmse:0.440821 test-rmse:0.477380 [12] train-rmse:0.400494 test-rmse:0.449140 [13] train-rmse:0.378058 test-rmse:0.435773 [14] train-rmse:0.366403 test-rmse:0.430393 [15] train-rmse:0.359765 test-rmse:0.427581 [16] train-rmse:0.356279 test-rmse:0.427452 [17] train-rmse:0.353901 test-rmse:0.428259 [18] train-rmse:0.352319 test-rmse:0.428241 [19] train-rmse:0.351136 test-rmse:0.427969 Stopping. Best iteration: [16] train-rmse:0.356279 test-rmse:0.427452 [1] train-rmse:8.762284 test-rmse:8.832029 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.142087 test-rmse:6.216422 [3] train-rmse:4.309922 test-rmse:4.385150 [4] train-rmse:3.029981 test-rmse:3.112198 [5] train-rmse:2.138312 test-rmse:2.224843 [6] train-rmse:1.519920 test-rmse:1.611128 [7] train-rmse:1.095359 test-rmse:1.189477 [8] train-rmse:0.808398 test-rmse:0.904603 [9] train-rmse:0.620372 test-rmse:0.717170 [10] train-rmse:0.501954 test-rmse:0.596902 [11] train-rmse:0.430710 test-rmse:0.521767 [12] train-rmse:0.390069 test-rmse:0.476869 [13] train-rmse:0.367907 test-rmse:0.450016 [14] train-rmse:0.356428 test-rmse:0.434145 [15] train-rmse:0.349307 test-rmse:0.424926 [16] train-rmse:0.345079 test-rmse:0.419318 [17] train-rmse:0.341431 test-rmse:0.416319 [18] train-rmse:0.338243 test-rmse:0.414718 [19] train-rmse:0.337617 test-rmse:0.413184 [20] train-rmse:0.335865 test-rmse:0.412910 [1] train-rmse:8.785597 test-rmse:8.670729 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.157600 test-rmse:6.048097 [3] train-rmse:4.319709 test-rmse:4.220972 [4] train-rmse:3.035725 test-rmse:2.954457 [5] train-rmse:2.140785 test-rmse:2.084866 [6] train-rmse:1.520001 test-rmse:1.489416 [7] train-rmse:1.093003 test-rmse:1.103211 [8] train-rmse:0.804244 test-rmse:0.863058 [9] train-rmse:0.614298 test-rmse:0.727083 [10] train-rmse:0.493737 test-rmse:0.657573 [11] train-rmse:0.421232 test-rmse:0.626477 [12] train-rmse:0.378633 test-rmse:0.614907 [13] train-rmse:0.355271 test-rmse:0.612254 [14] train-rmse:0.342913 test-rmse:0.611533 [15] train-rmse:0.336527 test-rmse:0.613855 [16] train-rmse:0.333037 test-rmse:0.615686 [17] train-rmse:0.330875 test-rmse:0.617808 Stopping. Best iteration: [14] train-rmse:0.342913 test-rmse:0.611533 [1] train-rmse:8.764179 test-rmse:8.814554 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.143318 test-rmse:6.185514 [3] train-rmse:4.310631 test-rmse:4.351260 [4] train-rmse:3.030592 test-rmse:3.067822 [5] train-rmse:2.138688 test-rmse:2.174898 [6] train-rmse:1.520173 test-rmse:1.561781 [7] train-rmse:1.095156 test-rmse:1.148014 [8] train-rmse:0.808426 test-rmse:0.876976 [9] train-rmse:0.619743 test-rmse:0.707292 [10] train-rmse:0.500985 test-rmse:0.609899 [11] train-rmse:0.429339 test-rmse:0.555310 [12] train-rmse:0.388302 test-rmse:0.527919 [13] train-rmse:0.365268 test-rmse:0.514205 [14] train-rmse:0.352236 test-rmse:0.508710 [15] train-rmse:0.345665 test-rmse:0.506257 [16] train-rmse:0.340659 test-rmse:0.504834 [17] train-rmse:0.337359 test-rmse:0.503867 [18] train-rmse:0.336118 test-rmse:0.503993 [19] train-rmse:0.334112 test-rmse:0.504842 [20] train-rmse:0.333558 test-rmse:0.505364 Stopping. Best iteration: [17] train-rmse:0.337359 test-rmse:0.503867 [1] train-rmse:8.770873 test-rmse:8.758064 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.147899 test-rmse:6.141793 [3] train-rmse:4.313589 test-rmse:4.319681 [4] train-rmse:3.032419 test-rmse:3.050954 [5] train-rmse:2.139779 test-rmse:2.179881 [6] train-rmse:1.520803 test-rmse:1.594484 [7] train-rmse:1.095524 test-rmse:1.211741 [8] train-rmse:0.808090 test-rmse:0.977631 [9] train-rmse:0.619187 test-rmse:0.842067 [10] train-rmse:0.500134 test-rmse:0.770155 [11] train-rmse:0.427174 test-rmse:0.735680 [12] train-rmse:0.384892 test-rmse:0.719645 [13] train-rmse:0.361760 test-rmse:0.714306 [14] train-rmse:0.349233 test-rmse:0.713087 [15] train-rmse:0.342928 test-rmse:0.712655 [16] train-rmse:0.339091 test-rmse:0.712648 [17] train-rmse:0.336033 test-rmse:0.713221 [18] train-rmse:0.335094 test-rmse:0.714010 [19] train-rmse:0.334287 test-rmse:0.714437 Stopping. Best iteration: [16] train-rmse:0.339091 test-rmse:0.712648
Warning message in `[.data.table`(dt_model_sub, , `:=`((incomplete_cols), NULL)): "length(LHS)==0; no columns to delete or assign RHS to."
[1] train-rmse:8.875637 test-rmse:8.946206 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.226021 test-rmse:6.299860 [3] train-rmse:4.374984 test-rmse:4.453355 [4] train-rmse:3.083682 test-rmse:3.170388 [5] train-rmse:2.184730 test-rmse:2.283771 [6] train-rmse:1.565196 test-rmse:1.678538 [7] train-rmse:1.143104 test-rmse:1.278177 [8] train-rmse:0.863466 test-rmse:1.020087 [9] train-rmse:0.682608 test-rmse:0.859915 [10] train-rmse:0.566762 test-rmse:0.765222 [11] train-rmse:0.497258 test-rmse:0.711946 [12] train-rmse:0.458463 test-rmse:0.684172 [13] train-rmse:0.435749 test-rmse:0.666368 [14] train-rmse:0.420187 test-rmse:0.657350 [15] train-rmse:0.408499 test-rmse:0.658700 [16] train-rmse:0.404773 test-rmse:0.655831 [17] train-rmse:0.400493 test-rmse:0.654012 [18] train-rmse:0.393103 test-rmse:0.659776 [19] train-rmse:0.390336 test-rmse:0.658623 [20] train-rmse:0.382649 test-rmse:0.658604 Stopping. Best iteration: [17] train-rmse:0.400493 test-rmse:0.654012 [1] train-rmse:8.893708 test-rmse:8.755706 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.237265 test-rmse:6.131278 [3] train-rmse:4.380857 test-rmse:4.320721 [4] train-rmse:3.084982 test-rmse:3.090445 [5] train-rmse:2.183708 test-rmse:2.281420 [6] train-rmse:1.560939 test-rmse:1.773379 [7] train-rmse:1.135961 test-rmse:1.480525 [8] train-rmse:0.851737 test-rmse:1.319989 [9] train-rmse:0.666003 test-rmse:1.252672 [10] train-rmse:0.550047 test-rmse:1.224309 [11] train-rmse:0.479727 test-rmse:1.206578 [12] train-rmse:0.437876 test-rmse:1.206968 [13] train-rmse:0.409683 test-rmse:1.211660 [14] train-rmse:0.396920 test-rmse:1.215792 Stopping. Best iteration: [11] train-rmse:0.479727 test-rmse:1.206578 [1] train-rmse:8.898838 test-rmse:8.798266 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.241273 test-rmse:6.146724 [3] train-rmse:4.384254 test-rmse:4.298433 [4] train-rmse:3.088932 test-rmse:3.018585 [5] train-rmse:2.188408 test-rmse:2.135464 [6] train-rmse:1.567068 test-rmse:1.541073 [7] train-rmse:1.143591 test-rmse:1.156635 [8] train-rmse:0.854206 test-rmse:0.915730 [9] train-rmse:0.665582 test-rmse:0.780188 [10] train-rmse:0.547702 test-rmse:0.713764 [11] train-rmse:0.477574 test-rmse:0.687458 [12] train-rmse:0.437383 test-rmse:0.672424 [13] train-rmse:0.410278 test-rmse:0.670858 [14] train-rmse:0.397520 test-rmse:0.672457 [15] train-rmse:0.389120 test-rmse:0.668446 [16] train-rmse:0.384001 test-rmse:0.666531 [17] train-rmse:0.378963 test-rmse:0.670512 [18] train-rmse:0.375277 test-rmse:0.671190 [19] train-rmse:0.369160 test-rmse:0.673432 Stopping. Best iteration: [16] train-rmse:0.384001 test-rmse:0.666531 [1] train-rmse:8.894723 test-rmse:8.806874 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.238278 test-rmse:6.164674 [3] train-rmse:4.381995 test-rmse:4.328818 [4] train-rmse:3.087709 test-rmse:3.063600 [5] train-rmse:2.188230 test-rmse:2.209735 [6] train-rmse:1.567448 test-rmse:1.641210 [7] train-rmse:1.144776 test-rmse:1.283513 [8] train-rmse:0.864006 test-rmse:1.071501 [9] train-rmse:0.675839 test-rmse:0.952468 [10] train-rmse:0.559159 test-rmse:0.894894 [11] train-rmse:0.487856 test-rmse:0.867339 [12] train-rmse:0.440369 test-rmse:0.856599 [13] train-rmse:0.417900 test-rmse:0.853359 [14] train-rmse:0.403265 test-rmse:0.848899 [15] train-rmse:0.393195 test-rmse:0.850130 [16] train-rmse:0.386328 test-rmse:0.851460 [17] train-rmse:0.382740 test-rmse:0.854124 Stopping. Best iteration: [14] train-rmse:0.403265 test-rmse:0.848899 [1] train-rmse:8.876709 test-rmse:8.924455 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.226289 test-rmse:6.285814 [3] train-rmse:4.374488 test-rmse:4.450537 [4] train-rmse:3.082899 test-rmse:3.171089 [5] train-rmse:2.185500 test-rmse:2.296657 [6] train-rmse:1.566519 test-rmse:1.710716 [7] train-rmse:1.146058 test-rmse:1.334482 [8] train-rmse:0.867080 test-rmse:1.103328 [9] train-rmse:0.679791 test-rmse:0.971539 [10] train-rmse:0.567735 test-rmse:0.902764 [11] train-rmse:0.494905 test-rmse:0.867924 [12] train-rmse:0.457603 test-rmse:0.849862 [13] train-rmse:0.436778 test-rmse:0.842401 [14] train-rmse:0.425778 test-rmse:0.838783 [15] train-rmse:0.416138 test-rmse:0.840372 [16] train-rmse:0.406558 test-rmse:0.841667 [17] train-rmse:0.402810 test-rmse:0.841675 Stopping. Best iteration: [14] train-rmse:0.425778 test-rmse:0.838783 [1] "overall test rmse:"
#imp_table_bb = imp_table_bb[order(-avg_gain)]
#imp_table_bb[, .SD[1], .(borough,b_class_group)][order(-avg_gain)]
dt_tree = imp_table_bb_wo[,.(M = mean(avg_gain, na.rm = TRUE), N = .N) ,.(b_class_group,Feature)]
options(repr.plot.width = 8, repr.plot.height = 5, repr.plot.res = 200)
ggplot(dt_tree
, aes(area = M, fill = N, label = Feature,subgroup = b_class_group)) +
geom_treemap() +
geom_treemap_subgroup_border() +
geom_treemap_subgroup_text(place = "centre", grow = T, alpha = 0.5, colour =
"black", fontface = "italic", min.size = 0) +
geom_treemap_text(colour = "white", place = "topleft", reflow = T)
feature_list = c( "zipcode","commercialunits_group","residentialunits_group","highly_commercial","onlycommercial"
,"address_encoded","taxclass_present","building_clusters","assessment_ratio_present"
,"grosssquarefeet_log_filled")
target = "saleprice_log"
dt_model = copy(dt[b_class_group =="other",.SD,.SDcols = c("idx","b_class_group",target,feature_list)])
chunk_no = 10
set.seed(0)
folds <- cut(seq(1,nrow(dt_model)),breaks=chunk_no,labels=FALSE)
pred_table = data.table()
imp_table = data.table()
fitted_table = data.table()
for(i in 1:chunk_no){
#Segment your data by fold using the which() function
testIndexes <- which(folds==i,arr.ind=TRUE)
testData <- dt_model[testIndexes, ]
trainData <- dt_model[-testIndexes, ]
y_train = trainData[[target]]
y_test = testData[[target]]
Scale_Parameters = get_scale_params(trainData, feature_list)
x_train = scale(trainData[,.SD,.SDcols = feature_list])
x_test = testData[,.SD,.SDcols = feature_list]
scale_external(x_test,Scale_Parameters)
d_train= xgb.DMatrix(data = as.matrix(x_train), label = y_train)
d_test = xgb.DMatrix(data = as.matrix(x_test), label = y_test)
set.seed(i)
xg.model = xgb.train( data = d_train,
nrounds = 20,
early_stopping_rounds = 3,
params = params,
watchlist = list(train = d_train, test = d_test))
if(str_detect(target,"log") == TRUE){
xg.pred = exp(predict(xg.model, d_test))
actual = exp(y_test)
xg.fitted = exp(predict(xg.model, d_train))
}else{
xg.pred = predict(xg.model, d_test)
actual = y_test
xg.fitted = exp(predict(xg.model, d_train))
}
imp = data.table(xgb.importance( feature_names = colnames(x_train), model = xg.model))
imp_table = rbind(imp_table,data.table(chunk = i, imp))
#Check performance
sub_pred_table = testData[,.(idx, actual = actual, pred = xg.pred, chunk = i)]
sub_fitted_table = trainData[,.(idx, fitted = xg.fitted, chunk = i )]
pred_table = rbind(pred_table,sub_pred_table )
fitted_table = rbind(fitted_table,sub_fitted_table )
}
[1] train-rmse:9.475795 test-rmse:10.592774 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.696434 test-rmse:7.674856 [3] train-rmse:4.770021 test-rmse:5.616743 [4] train-rmse:3.445459 test-rmse:4.218417 [5] train-rmse:2.544741 test-rmse:3.526853 [6] train-rmse:1.947937 test-rmse:3.124066 [7] train-rmse:1.558087 test-rmse:2.949484 [8] train-rmse:1.322696 test-rmse:2.931334 [9] train-rmse:1.182355 test-rmse:2.998362 [10] train-rmse:1.097964 test-rmse:3.025176 [11] train-rmse:1.046365 test-rmse:3.128329 Stopping. Best iteration: [8] train-rmse:1.322696 test-rmse:2.931334 [1] train-rmse:9.583471 test-rmse:9.526132 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.774062 test-rmse:6.678609 [3] train-rmse:4.824202 test-rmse:4.777328 [4] train-rmse:3.478246 test-rmse:3.482120 [5] train-rmse:2.564366 test-rmse:2.635166 [6] train-rmse:1.957802 test-rmse:2.068422 [7] train-rmse:1.565923 test-rmse:1.733091 [8] train-rmse:1.319340 test-rmse:1.529237 [9] train-rmse:1.176938 test-rmse:1.421105 [10] train-rmse:1.080906 test-rmse:1.353161 [11] train-rmse:1.032939 test-rmse:1.324065 [12] train-rmse:1.005731 test-rmse:1.313069 [13] train-rmse:0.988327 test-rmse:1.297879 [14] train-rmse:0.969499 test-rmse:1.291362 [15] train-rmse:0.962956 test-rmse:1.290727 [16] train-rmse:0.952878 test-rmse:1.291314 [17] train-rmse:0.937877 test-rmse:1.290844 [18] train-rmse:0.934220 test-rmse:1.290032 [19] train-rmse:0.924822 test-rmse:1.294205 [20] train-rmse:0.922535 test-rmse:1.292432 [1] train-rmse:9.606288 test-rmse:9.343801 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.789824 test-rmse:6.555468 [3] train-rmse:4.836308 test-rmse:4.631732 [4] train-rmse:3.492227 test-rmse:3.337711 [5] train-rmse:2.575355 test-rmse:2.469343 [6] train-rmse:1.963380 test-rmse:1.962725 [7] train-rmse:1.570695 test-rmse:1.624777 [8] train-rmse:1.320171 test-rmse:1.409063 [9] train-rmse:1.171436 test-rmse:1.311739 [10] train-rmse:1.086307 test-rmse:1.260468 [11] train-rmse:1.027302 test-rmse:1.235441 [12] train-rmse:0.992815 test-rmse:1.224892 [13] train-rmse:0.973819 test-rmse:1.223816 [14] train-rmse:0.962317 test-rmse:1.228915 [15] train-rmse:0.955480 test-rmse:1.227153 [16] train-rmse:0.947020 test-rmse:1.230049 Stopping. Best iteration: [13] train-rmse:0.973819 test-rmse:1.223816 [1] train-rmse:9.576550 test-rmse:9.741603 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.770229 test-rmse:6.948622 [3] train-rmse:4.824609 test-rmse:4.970980 [4] train-rmse:3.482348 test-rmse:3.615219 [5] train-rmse:2.571593 test-rmse:2.710220 [6] train-rmse:1.966844 test-rmse:2.110519 [7] train-rmse:1.570960 test-rmse:1.701250 [8] train-rmse:1.334578 test-rmse:1.457409 [9] train-rmse:1.184458 test-rmse:1.332526 [10] train-rmse:1.091927 test-rmse:1.234456 [11] train-rmse:1.044401 test-rmse:1.192280 [12] train-rmse:1.015574 test-rmse:1.170931 [13] train-rmse:0.997466 test-rmse:1.156125 [14] train-rmse:0.975450 test-rmse:1.156074 [15] train-rmse:0.966782 test-rmse:1.151108 [16] train-rmse:0.955908 test-rmse:1.146930 [17] train-rmse:0.937783 test-rmse:1.147163 [18] train-rmse:0.929422 test-rmse:1.133181 [19] train-rmse:0.927968 test-rmse:1.132573 [20] train-rmse:0.921034 test-rmse:1.130989 [1] train-rmse:9.609530 test-rmse:9.262072 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.788953 test-rmse:6.487993 [3] train-rmse:4.832179 test-rmse:4.595708 [4] train-rmse:3.482332 test-rmse:3.271686 [5] train-rmse:2.560445 test-rmse:2.421708 [6] train-rmse:1.946611 test-rmse:1.906741 [7] train-rmse:1.552367 test-rmse:1.656408 [8] train-rmse:1.301458 test-rmse:1.532557 [9] train-rmse:1.152679 test-rmse:1.485062 [10] train-rmse:1.064791 test-rmse:1.487538 [11] train-rmse:1.018153 test-rmse:1.506422 [12] train-rmse:0.984961 test-rmse:1.521010 Stopping. Best iteration: [9] train-rmse:1.152679 test-rmse:1.485062 [1] train-rmse:9.586270 test-rmse:9.567161 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.775471 test-rmse:6.790532 [3] train-rmse:4.824172 test-rmse:4.845938 [4] train-rmse:3.480150 test-rmse:3.512642 [5] train-rmse:2.564955 test-rmse:2.613870 [6] train-rmse:1.959582 test-rmse:2.057150 [7] train-rmse:1.567076 test-rmse:1.700578 [8] train-rmse:1.328517 test-rmse:1.517204 [9] train-rmse:1.173601 test-rmse:1.414101 [10] train-rmse:1.089313 test-rmse:1.379457 [11] train-rmse:1.031790 test-rmse:1.366724 [12] train-rmse:1.000044 test-rmse:1.358961 [13] train-rmse:0.986177 test-rmse:1.359613 [14] train-rmse:0.972158 test-rmse:1.353437 [15] train-rmse:0.965193 test-rmse:1.353377 [16] train-rmse:0.956516 test-rmse:1.359041 [17] train-rmse:0.954853 test-rmse:1.355327 [18] train-rmse:0.947948 test-rmse:1.356183 Stopping. Best iteration: [15] train-rmse:0.965193 test-rmse:1.353377 [1] train-rmse:9.557076 test-rmse:9.955791 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.756503 test-rmse:7.158644 [3] train-rmse:4.810574 test-rmse:5.175217 [4] train-rmse:3.473160 test-rmse:3.825783 [5] train-rmse:2.562641 test-rmse:2.854491 [6] train-rmse:1.955599 test-rmse:2.238019 [7] train-rmse:1.563697 test-rmse:1.803235 [8] train-rmse:1.321373 test-rmse:1.547157 [9] train-rmse:1.171168 test-rmse:1.385025 [10] train-rmse:1.081276 test-rmse:1.286333 [11] train-rmse:1.028083 test-rmse:1.227438 [12] train-rmse:0.987251 test-rmse:1.185383 [13] train-rmse:0.969323 test-rmse:1.163868 [14] train-rmse:0.954524 test-rmse:1.147621 [15] train-rmse:0.935630 test-rmse:1.142146 [16] train-rmse:0.922859 test-rmse:1.143774 [17] train-rmse:0.911629 test-rmse:1.140501 [18] train-rmse:0.903009 test-rmse:1.138969 [19] train-rmse:0.898641 test-rmse:1.138482 [20] train-rmse:0.897267 test-rmse:1.137872 [1] train-rmse:9.590735 test-rmse:9.572249 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.780594 test-rmse:6.780503 [3] train-rmse:4.832287 test-rmse:4.863914 [4] train-rmse:3.485816 test-rmse:3.549873 [5] train-rmse:2.574097 test-rmse:2.653762 [6] train-rmse:1.966635 test-rmse:2.077515 [7] train-rmse:1.575134 test-rmse:1.682754 [8] train-rmse:1.334083 test-rmse:1.464845 [9] train-rmse:1.194095 test-rmse:1.316727 [10] train-rmse:1.108044 test-rmse:1.238530 [11] train-rmse:1.058320 test-rmse:1.185454 [12] train-rmse:1.020358 test-rmse:1.167393 [13] train-rmse:0.998347 test-rmse:1.146945 [14] train-rmse:0.985744 test-rmse:1.132418 [15] train-rmse:0.973689 test-rmse:1.124357 [16] train-rmse:0.969959 test-rmse:1.117049 [17] train-rmse:0.962814 test-rmse:1.114319 [18] train-rmse:0.949709 test-rmse:1.112326 [19] train-rmse:0.940798 test-rmse:1.109283 [20] train-rmse:0.937003 test-rmse:1.121840 [1] train-rmse:9.606738 test-rmse:9.301232 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.791099 test-rmse:6.473090 [3] train-rmse:4.838437 test-rmse:4.527308 [4] train-rmse:3.497327 test-rmse:3.192652 [5] train-rmse:2.581876 test-rmse:2.344828 [6] train-rmse:1.976440 test-rmse:1.781091 [7] train-rmse:1.592994 test-rmse:1.439218 [8] train-rmse:1.344796 test-rmse:1.239241 [9] train-rmse:1.199573 test-rmse:1.136228 [10] train-rmse:1.109209 test-rmse:1.082895 [11] train-rmse:1.052723 test-rmse:1.057884 [12] train-rmse:1.025309 test-rmse:1.048348 [13] train-rmse:1.008376 test-rmse:1.041796 [14] train-rmse:0.995994 test-rmse:1.042012 [15] train-rmse:0.981820 test-rmse:1.048071 [16] train-rmse:0.970038 test-rmse:1.059126 Stopping. Best iteration: [13] train-rmse:1.008376 test-rmse:1.041796 [1] train-rmse:9.644371 test-rmse:8.846104 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:6.817543 test-rmse:6.075935 [3] train-rmse:4.857388 test-rmse:4.195844 [4] train-rmse:3.504084 test-rmse:2.918603 [5] train-rmse:2.585797 test-rmse:1.954818 [6] train-rmse:1.978708 test-rmse:1.432465 [7] train-rmse:1.582806 test-rmse:1.237175 [8] train-rmse:1.333716 test-rmse:1.150485 [9] train-rmse:1.181110 test-rmse:1.219774 [10] train-rmse:1.097499 test-rmse:1.250147 [11] train-rmse:1.049614 test-rmse:1.330411 Stopping. Best iteration: [8] train-rmse:1.333716 test-rmse:1.150485
pred_table = merge(pred_table, dt[,.(idx,b_class_present, taxclass_present)], by = "idx", all.x = TRUE )
options(repr.plot.width = 10, repr.plot.height = 10, repr.plot.res = 200) # for graph sizes
ggplot(data = pred_table[actual < 1000000 & pred < 750000], aes(x = actual, y = pred, color = as.factor(taxclass_present))) + geom_point() +
geom_abline(intercept = 0, slope = 1) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5)) +
facet_wrap(~b_class_present)
dt_ts = copy(dt)
dt_ts[, year := year(saledate)]
dt_ts[, wday := wday(saledate)]
dt_ts[, month := month(saledate)]
dt_ts[, week := week(saledate)]
month_dt= dt_ts[,.(month_avg_saleprice = mean(saleprice),
month_num_saleprice = .N),
.(month,borough,b_class_group)]
month_dt = month_dt[order(month)]
month_dt[,`:=` (month_avg_saleprice = shift(month_avg_saleprice,1,type = "lag"),
month_num_saleprice = shift(month_num_saleprice,1,type = "lag")),
.(borough,b_class_group)]
week_dt= dt_ts[,.(week_avg_saleprice = mean(saleprice),
week_num_saleprice = .N),
.(week,borough,b_class_group)]
week_dt = week_dt[order(week)]
week_dt[,`:=` ( week_avg_saleprice = shift(week_avg_saleprice,1,type = "lag"),
week_num_saleprice = shift(week_num_saleprice,1,type = "lag")),
.(borough,b_class_group)]
dt_ts = merge(dt_ts,month_dt, by = c("borough","b_class_group","month") )
dt_ts = merge(dt_ts,week_dt, by = c("borough","b_class_group","week") )
dt_ts = dt_ts[order(saledate)]
params = list( booster = "gbtree"
# , eta = best_params$eta #learning rate
# , gamma = best_params$gamma # min loss reduction
# , max_depth = best_params$depth
, min_child_weight = 1, subsample = 1, colsample_bytree= 1
, objective = "reg:squarederror"
, eval_metric = "rmse")
feature_list = c("borough","zipcode","residentialunits","commercialunits","address_encoded","b_class_present_encoded","taxclassatpresent_encoded","week_avg_saleprice","week_num_saleprice","month_avg_saleprice","month_num_saleprice")
target = "saleprice"
dt_ts_model = copy(dt_ts[,.SD,.SDcols = c("idx","month","year",target,feature_list)])
imp_table = data.table()
xg_preds = c()
for(i in c(1:7)){
x_train_sub = dt_ts_model[(month <= i & year == 2017)| year == 2016,.SD,.SDcols = feature_list]
Scale_Parameters = get_scale_params(x_train_sub)
x_train_sub = scale(x_train_sub, center = TRUE, scale = TRUE)
x_test_sub = dt_ts_model[((month == i+1) & year == 2017) ,.SD,.SDcols = feature_list]
scale_external(x_test_sub,Scale_Parameters)
y_train_sub = dt_ts_model[(month <= i & year == 2017)| year == 2016][[target]]
y_test_sub = dt_ts_model[(month == 1+i) & year == 2017][[target]]
d_train_sub= xgb.DMatrix(data = as.matrix(x_train_sub), label = y_train_sub)
d_test_sub = xgb.DMatrix(data = as.matrix(x_test_sub), label = y_test_sub)
set.seed(0)
xg.model_sub = xgb.train( data = d_train_sub,
nrounds = 30,
early_stopping_rounds = 3,
params = params,
watchlist = list(train = d_train_sub, test = d_test_sub))
imp = data.table(xgb.importance( feature_names = colnames(x_train_sub), model = xg.model_sub))
imp_table = rbind(imp_table,data.table(month = i, imp))
xg.pred_sub = predict(xg.model_sub, d_test_sub)
pred = xg.pred_sub
xg_preds = c(xg_preds,pred)
}
[1] train-rmse:9704705.000000 test-rmse:3296821.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:8531835.000000 test-rmse:3199848.250000 [3] train-rmse:7619855.500000 test-rmse:3135980.500000 [4] train-rmse:6903849.500000 test-rmse:3245223.000000 [5] train-rmse:6360238.000000 test-rmse:3224500.000000 [6] train-rmse:5901778.500000 test-rmse:3338857.250000 Stopping. Best iteration: [3] train-rmse:7619855.500000 test-rmse:3135980.500000 [1] train-rmse:9033909.000000 test-rmse:5983018.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7960217.000000 test-rmse:5866709.000000 [3] train-rmse:7116164.000000 test-rmse:5796786.500000 [4] train-rmse:6480772.500000 test-rmse:5768605.500000 [5] train-rmse:5972284.000000 test-rmse:5737396.500000 [6] train-rmse:5558573.500000 test-rmse:5727986.500000 [7] train-rmse:5230020.500000 test-rmse:5704078.500000 [8] train-rmse:4960553.000000 test-rmse:5683572.000000 [9] train-rmse:4756313.000000 test-rmse:5679014.500000 [10] train-rmse:4603859.000000 test-rmse:5676062.500000 [11] train-rmse:4414274.500000 test-rmse:5739276.000000 [12] train-rmse:4290699.000000 test-rmse:5750426.000000 [13] train-rmse:4201697.000000 test-rmse:5875419.000000 Stopping. Best iteration: [10] train-rmse:4603859.000000 test-rmse:5676062.500000 [1] train-rmse:8643391.000000 test-rmse:4993472.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7683608.000000 test-rmse:4832412.000000 [3] train-rmse:6947946.000000 test-rmse:4707531.000000 [4] train-rmse:6369639.000000 test-rmse:4650448.000000 [5] train-rmse:5927390.500000 test-rmse:4605252.500000 [6] train-rmse:5574102.000000 test-rmse:4525333.500000 [7] train-rmse:5283706.000000 test-rmse:4521103.500000 [8] train-rmse:5061386.500000 test-rmse:4541742.000000 [9] train-rmse:4928424.000000 test-rmse:4533760.000000 [10] train-rmse:4777950.000000 test-rmse:4536626.500000 Stopping. Best iteration: [7] train-rmse:5283706.000000 test-rmse:4521103.500000 [1] train-rmse:8298494.500000 test-rmse:32377436.000000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:7396681.000000 test-rmse:31600054.000000 [3] train-rmse:6681528.500000 test-rmse:31211704.000000 [4] train-rmse:6142213.000000 test-rmse:31185622.000000 [5] train-rmse:5729278.000000 test-rmse:30994954.000000 [6] train-rmse:5374735.000000 test-rmse:30893024.000000 [7] train-rmse:5125364.000000 test-rmse:30791982.000000 [8] train-rmse:4989856.000000 test-rmse:30788224.000000 [9] train-rmse:4824968.000000 test-rmse:30786130.000000 [10] train-rmse:4680625.500000 test-rmse:30769446.000000 [11] train-rmse:4587430.000000 test-rmse:30760980.000000 [12] train-rmse:4498915.000000 test-rmse:30749114.000000 [13] train-rmse:4394922.000000 test-rmse:30716506.000000 [14] train-rmse:4270185.500000 test-rmse:30714648.000000 [15] train-rmse:4258726.000000 test-rmse:30784946.000000 [16] train-rmse:4236502.000000 test-rmse:30805966.000000 [17] train-rmse:4223927.000000 test-rmse:30859646.000000 Stopping. Best iteration: [14] train-rmse:4270185.500000 test-rmse:30714648.000000 [1] train-rmse:12223362.000000 test-rmse:3139955.250000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:10567563.000000 test-rmse:3276170.000000 [3] train-rmse:9243325.000000 test-rmse:3724770.500000 [4] train-rmse:8173453.000000 test-rmse:4283304.000000 Stopping. Best iteration: [1] train-rmse:12223362.000000 test-rmse:3139955.250000 [1] train-rmse:11515921.000000 test-rmse:4486000.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9973459.000000 test-rmse:4430153.500000 [3] train-rmse:8743417.000000 test-rmse:4302638.000000 [4] train-rmse:7789837.500000 test-rmse:4320350.000000 [5] train-rmse:7000547.000000 test-rmse:4325205.000000 [6] train-rmse:6436900.000000 test-rmse:4379829.500000 Stopping. Best iteration: [3] train-rmse:8743417.000000 test-rmse:4302638.000000 [1] train-rmse:11110113.000000 test-rmse:4487915.500000 Multiple eval metrics are present. Will use test_rmse for early stopping. Will train until test_rmse hasn't improved in 3 rounds. [2] train-rmse:9628339.000000 test-rmse:4308925.000000 [3] train-rmse:8462134.000000 test-rmse:4251317.000000 [4] train-rmse:7612267.000000 test-rmse:4186774.750000 [5] train-rmse:6853718.000000 test-rmse:4170652.250000 [6] train-rmse:6350556.500000 test-rmse:4191768.500000 [7] train-rmse:5901671.500000 test-rmse:4212039.000000 [8] train-rmse:5568236.500000 test-rmse:4257759.000000 Stopping. Best iteration: [5] train-rmse:6853718.000000 test-rmse:4170652.250000
imp_table_2= dcast(imp_table, Feature ~ month, value.var = "Gain")
imp_table_2[order(-`5`)]
| Feature | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| zipcode | 0.3666863584091 | 0.3718584866 | 0.3898320650 | 0.402333926 | 0.380012206237241 | 0.3916600838 | 0.3574691807 |
| week_num_saleprice | 0.0449917220809 | 0.0519866526 | 0.0083080747 | 0.014392801 | 0.247750492517238 | 0.2517739687 | 0.2551718402 |
| commercialunits | 0.2526496385865 | 0.2327231346 | 0.2226555662 | 0.248431893 | 0.242729263241704 | 0.2364598955 | 0.2640867928 |
| residentialunits | 0.1474168886052 | 0.1616791410 | 0.1811951062 | 0.166163344 | 0.065129681937992 | 0.0549987363 | 0.0492730087 |
| week_avg_saleprice | 0.1237781057110 | 0.1078047202 | 0.1205063003 | 0.089424938 | 0.032146072694874 | 0.0306743934 | 0.0273158565 |
| month_avg_saleprice | 0.0468876410091 | 0.0409448106 | 0.0487944458 | 0.038808649 | 0.024906915765110 | 0.0185985441 | 0.0181011905 |
| b_class_present_encoded | 0.0142383185164 | 0.0227154113 | 0.0171771435 | 0.028891929 | 0.004677509623465 | 0.0039349164 | 0.0064462078 |
| month_num_saleprice | 0.0002260518082 | 0.0007604329 | 0.0022693525 | 0.005244080 | 0.002012431498698 | 0.0028166909 | 0.0058921134 |
| address_encoded | 0.0031243076786 | 0.0078576564 | 0.0067589415 | 0.003883135 | 0.000635423079412 | 0.0017445197 | 0.0015336999 |
| borough | 0.0000009675949 | 0.0014255472 | 0.0009696438 | 0.001240353 | 0.000000003404265 | 0.0070677899 | 0.0142660016 |
| taxclassatpresent_encoded | NA | 0.0002440066 | 0.0015333607 | 0.001184953 | NA | 0.0002704612 | 0.0004441078 |
print("overall test rmse:")
calc_rmse(pred_table$pred,pred_table$actual)
calc_rmse(pred_table[actual < 20000000]$pred,pred_table[actual < 20000000]$actual)
[1] "overall test rmse:"
Best model seems to be saleprice_log with zipcode, trained without outliers.
zipcode improves the model performance.
outliers in he prediction set causes some sort of tradeoff.
improve the test performance: predict outlier observations better in the expense of predicting the regular data points worse
improve the prediction accuracy of as many data points as possible : diverge from the outlier observations and predict regular data points better
There should be some sort of distinction between outlier and regular data points. However, I could not find it. Therefore, I could not predict them in separate models. That's why I could not escape the tradeoff I mentioned above.
pred_table_bb_wo[, error := actual - pred]
pred_table_bb_wo[order(-abs(error))] %>% head(10)
| idx | actual | pred | chunk | borough | b_class_group | error |
|---|---|---|---|---|---|---|
| <int> | <dbl> | <dbl> | <int> | <int> | <chr> | <dbl> |
| 7448 | 2210000000 | 10793659.67 | 3 | 1 | other | 2199206340 |
| 2560 | 1040000000 | 21152639.15 | 1 | 1 | other | 1018847361 |
| 2558 | 652000000 | 21152639.15 | 1 | 1 | other | 630847361 |
| 6333 | 620000000 | 231960.07 | 2 | 1 | d | 619768040 |
| 2051 | 565000000 | 22116462.74 | 1 | 1 | other | 542883537 |
| 35390 | 345000000 | 599979.80 | 3 | 3 | other | 344400020 |
| 6318 | 330000000 | 20168480.58 | 3 | 1 | other | 309831519 |
| 9595 | 268124175 | 8806109.56 | 4 | 1 | other | 259318065 |
| 66509 | 257500000 | 3650676.64 | 3 | 4 | other | 253849323 |
| 2091 | 239114603 | 68325.75 | 1 | 1 | d | 239046277 |
selected_data = pred_table_bb_wo[abs(error)> (mean(error)*1.6)]
selected_data = dt[idx %in% selected_data$idx]
nrow(selected_data)
selected_list = c("saleprice_log","borough","block","lot","zipcode","residentialunits","commercialunits","yearbuilt","assessment_ratio_present","address_encoded","b_class_group_encoded","taxclassatpresent_encoded")
selected_data = selected_data[,.SD,.SDcols = selected_list]
Scale_Parameters = get_scale_params(dt, selected_list)
scale_external(selected_data,Scale_Parameters)
set.seed(200)
clusters <- kmeans(selected_data, centers = 3, nstart= 15)
clusters
K-means clustering with 3 clusters of sizes 2459, 4162, 27 Cluster means: saleprice_log borough block lot zipcode residentialunits 1 1.424007 0.1167612 -0.4245743 -0.1073720 0.5457525 0.1819667 2 1.556850 -1.4375017 -0.8635838 0.7724970 -1.3560518 0.1457964 3 2.563738 -1.0123383 -0.4757837 -0.5399831 -0.9139562 29.5371330 commercialunits yearbuilt assessment_ratio_present address_encoded 1 0.09189132 -0.1453734 0.1838305 -0.1866311 2 0.03372353 -0.1339376 0.8992717 -0.2834275 3 8.80271019 0.2071931 0.9200065 -0.2165303 b_class_group_encoded taxclassatpresent_encoded 1 0.004520012 0.02449631 2 -0.557202874 -0.80047692 3 -0.819642380 -0.77968515 Clustering vector: [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [482] 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 3 3 2 2 [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 [852] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [889] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 1 2 2 2 2 2 2 2 2 2 [926] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [963] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1000] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1037] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 1 1 1 1 2 1 1 2 2 2 2 2 2 2 [1074] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1111] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1148] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1185] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1222] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1259] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 [1296] 3 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1333] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1370] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1407] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1444] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1481] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1518] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 [1555] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1592] 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 [1629] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1666] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1703] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1740] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1777] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1814] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1851] 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1888] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1925] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1962] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [1999] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2036] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2073] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2110] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2147] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2184] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 [2221] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2258] 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2295] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2332] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2369] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2406] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2443] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2480] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2517] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2554] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2591] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2628] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2665] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2702] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2739] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2776] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [2813] 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2850] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2887] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2924] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2961] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [2998] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 [3035] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3072] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3109] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3146] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 [3183] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 [3220] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3257] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3294] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3331] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3368] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3405] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3442] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3479] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3516] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 1 1 2 2 2 2 2 2 [3553] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3590] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3627] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3664] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3701] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3738] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3775] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3812] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3849] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3886] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3923] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3960] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3997] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 [4034] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [4071] 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [4108] 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [4145] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1 1 1 1 1 1 2 2 2 2 2 2 [4182] 1 1 1 1 2 2 2 2 2 2 3 2 1 2 2 2 1 1 1 1 1 1 1 1 2 1 2 2 2 2 1 1 1 1 1 1 1 [4219] 1 1 2 2 2 1 1 1 2 2 2 2 2 2 2 1 1 2 1 2 2 2 2 2 1 2 2 3 2 2 1 1 1 2 1 1 1 [4256] 1 1 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 3 2 2 2 1 1 1 [4293] 1 1 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 [4330] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 [4367] 1 2 1 2 2 2 2 2 1 2 2 1 1 2 2 3 2 1 1 2 2 1 1 2 1 2 1 2 2 2 2 1 2 1 1 1 1 [4404] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4441] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4478] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4515] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4552] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4589] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4626] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4663] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4700] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4737] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4774] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4811] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4848] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4885] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4922] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4959] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [4996] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5033] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5070] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5107] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5144] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5181] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5218] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5255] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5292] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5329] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5366] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5403] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5440] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5477] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5514] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5551] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5588] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5625] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5662] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5699] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5736] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5773] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5810] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5847] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5884] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5921] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5958] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [5995] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6032] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6069] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6106] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6143] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6180] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6217] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6254] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6291] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6328] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6365] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6402] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6439] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 [6476] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6513] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6550] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 [6587] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [6624] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Within cluster sum of squares by cluster: [1] 25424.62 33642.62 56488.21 (between_SS / total_SS = 25.0 %) Available components: [1] "cluster" "centers" "totss" "withinss" "tot.withinss" [6] "betweenss" "size" "iter" "ifault"
All are located close by an avenue.
All have residential and commercial units
table(clusters$cluster)
1 2 3 2459 4162 27
unique(dt[,.(address, address_encoded)])
| address | address_encoded |
|---|---|
| <chr> | <dbl> |
| avenue | 1 |
| street | 2 |
| other | 3 |
table(dt$taxclassatpresent)
1 1a 1b 1c 2 2a 2b 2c 4
24964 1095 305 130 25675 1255 399 1543 2429
I could not find a common property of outlier points :(
# with nys as (
# select nyx.*
# ,case when SALE_PRICE = '-' or SALE PRICE IS NULL then 0 else SALE_PRICE end as SALE_PRICE_cor
# ,COALESCE(grosssquarefeet,0) as grosssquarefeet_cor
# from nyx_rolling_sales nyx
# ),
# stats as (
# select nys.*
# ,avg(SALE_PRICE_cor) over() as mean
# ,stddev(SALE_PRICE_cor) over () as sd
# ,avg(SALE_PRICE_cor) over(partition by NEIGHBORHOOD,BUILDING_CLASS) as mean_nb
# ,stddev(SALE_PRICE_cor) over (partition by NEIGHBORHOOD,BUILDING_CLASS) as sd_nb
# from nys
# )
# select s.*
# , COALESCE(TRY((s.SALE_PRICE_cor - s.mean) / s.sd),0) as sale_price_zscore -- a
# , COALESCE(TRY((s.SALE_PRICE_cor - s.mean_nb) / s.sd_nb),0) as sale_price_zscore_neighborhood --b
# , COALESCE(TRY(grosssquarefeet_cor/totalunits),0) as square_ft_per_unit --c
# , COALESCE(TRY(s.SALE_PRICE_cor/totalunits),0) as price_per_unit --c
# from stats s
#